AUTOMATION HISTORY

From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Discover the engineering revolution transforming modern defense with Strength, Stealth, Speed: The Very Fast Future of Advanced Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Future of Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. The Seoul fashion show explored how humans and robots may coexist in everyday life. Humanoid robots wearing designer outfits walked alongside human models at a fashion show in Seoul this week, offering a glimpse into how South Korea’s technology industry imagines a future where robots are not just tools, but participants in everyday cultural life. The event, called the “Mach33: Physical AI Fashion Show,” was hosted by South Korean entertainment technology company Galaxy Corporation and featured robots and humans walking the runway together in coordinated outfits. Videos and images from the show depicted humanoid robots strutting down the catwalk, posing beside models, and performing synchronized choreography. According to Reuters, the event was designed around the idea of humans and robots coexisting in daily life, with matching outfits intended to imagine how future interactions between people and physical AI systems might look. The fashion show took place at Galaxy Robot Park in Seoul, a recently opened robot-themed entertainment complex that combines robotics, artificial intelligence, K-pop culture, and interactive attractions. Humanoid robots have traditionally been demonstrated in controlled industrial settings, research laboratories, or technology exhibitions focused on engineering capabilities. The Seoul event reflected a growing shift toward presenting robots in social, cultural, and entertainment environments rather than purely technical ones. According to Reuters and local South Korean media reports, the runway presentation featured robots dressed in designer clothing and paired with human models, with organizers describing the concept as a “physical AI” showcase. Galaxy Corporation has increasingly positioned itself as an “enter-tech” company that combines entertainment and advanced technology. The firm is also known for managing major Korean entertainment figures, including K-pop star G-Dragon. The fashion show was part of a broader effort by the company to expand robot-centered entertainment experiences. Reports indicate Galaxy also plans robot concerts, interactive performances, and additional AI-focused cultural events. The runway event arrives as South Korea continues to strengthen its position as one of the world’s most robot-intensive economies. South Korea has one of the highest robot densities globally, with more than 1,000 industrial robots for every 10,000 workers. The country has invested heavily in automation, advanced manufacturing, artificial intelligence, and humanoid robotics. In recent years, South Korea has also launched major initiatives to accelerate domestic humanoid robot development, including collaborations among technology firms, universities, and government-backed research programs. At the same time, robotics companies worldwide are increasingly attempting to move humanoid machines beyond factories and warehouses into environments where they interact more directly with people. That transition remains difficult. While modern humanoid robots have become significantly better at walking, balancing, dancing, and performing choreographed movements, researchers continue to face challenges involving dexterity, autonomy, perception, and natural human-robot interaction. Still, events like the Seoul runway show demonstrate how robotics is increasingly being presented not just as an industrial technology, but as part of broader discussions about culture, design, entertainment, and daily life. Whether robot fashion shows become a lasting trend or remain a technological novelty, the sight of humanoid machines sharing the catwalk with human models shows how quickly robotics is moving into spaces once considered uniquely human. Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder. Premium Follow

AI robotics company Figure posted several videos on X throughout May showcasing its robots performing basic tasks, including cleaning a room and sorting packages. Modern artificial intelligence-powered robots are impressive in their capabilities, but are still years away from replacing humans as they can’t yet adapt to changing conditions, researchers say. Last month, AI robotics company Figure showcased its humanoid robots performing basic tasks, such as cleaning a room, but a series of robots working for nine days straight sorting packages sparked conversation about how soon robots could replace jobs. Oliver Obst, an associate professor of robotics at the Australia based University of New South Wales, told Cointelegraph that repetitive jobs such as physical work in structured environments are currently most at risk of being replaced by robots, while administrative and document-processing tasks could be replaced by AI. There has been growing concern that AI and robots will replace people in jobs as technology advances. A report in May from workforce consulting firm Challenger, Gray and Christmas found that US companies have laid off an estimated 49,135 people in 2026 due to AI. A group of Figure’s robots worked for nine days straight sorting packages. Source: Figure However, Obst said that humanoid robots are unlikely to see a mass rollout soon because they don’t appear to be more efficient or less error-prone than current robotic manufacturing methods. “Even in relatively structured settings, they still face problems with reliability, speed, safety, cost, and recovery from unexpected situations,” he said. “The harder the environment is to control, the harder the robotics problem becomes. Most human jobs involve more variation and more judgment than the package-sorting demonstration.” In another video in May, a human worker managed to sort more packages compared to a team of Figure’s robots, which swapped out when needing a recharge. Figure CEO Brett Adock said it would be the last time “a human will ever win.” Source: Brett Adock Markus Levin, co-founder of decentralized data network XYO, said AI models and automation software can perform repetitive tasks with far greater consistency and endurance than humans; however, robots still require charging, maintenance and supervision. A report in September from the International Federation of Robotics found that global demand for factory robots has doubled over the last decade, with warehouses and logistics among the fastest-growing areas of adoption. “I believe broad human replacement is still likely years away,” Levin added, “Reliability, safety, regulation, infrastructure costs, and trust remain major barriers to full-scale deployment across society. The challenge is no longer simply making machines capable of acting but ensuring they can operate safely and reliably as they take on greater autonomy.” Dr Francisco Cruz Naranjo, a senior lecturer at the University of New South Wales with a PhD in robotics, said the efficiency of robots compared to people depends heavily on the activity and the environment. Related: ‘Developed ecosystem’ based on crypto has sprung up for AI agents: Report “Robots are much better at repetitive tasks without the need for constant pauses, as showcased in the Figure livestream. However, in highly dynamic environments, robots still struggle to quickly adapt to changing conditions,” he said. Naranjo said repetitive jobs performed in a less static setting are at risk of being replaced by robots, but it will depend on how quickly research advances and how quickly society adapts in areas like making spaces robot-friendly, which is likely years away. Naranjo and Obst said that a mass rollout of robots in the workforce could be of some benefit, such as improving work-life balance, increasing the workforce in areas with shortages, and addressing dangerous environments that are too risky for humans. “The social question is harder. If robots make dangerous work cheaper in human terms, that can be good. But it can also have unintended consequences. For example, keeping humans out of harm’s way in military operations may save lives, but it could also lower the perceived cost of conflict,” Obst said. Magazine: Korea’s first memecoin rug-pull case, China’s crypto rules review: Asia Express More on the subject Cointelegraph is committed to providing independent, high-quality journalism across the crypto, blockchain, AI, and fintech industries. All news, reviews, and analyses are produced with full journalistic independence and integrity. For more details on our standards and processes, please read our Editorial Policy.

Authored by Stephen Katte via Cointelegraph, AI robotics company Figure posted several videos on X throughout May showcasing its robots performing basic tasks, including cleaning a room and sorting packages. Modern artificial intelligence-powered robots are impressive in their capabilities, but are still years away from replacing humans as they can't yet adapt to changing conditions, researchers say. Last month, AI robotics company Figure showcased its humanoid robots performing basic tasks, such as cleaning a room, but a series of robots working for nine days straight sorting packages sparked conversation about how soon robots could replace jobs. Welcome to Day 9 of our humanoid livestream: 191 consecutive hours and 238,000 packages. Oliver Obst, an associate professor of robotics at the Australia based University of New South Wales, told Cointelegraph that repetitive jobs such as physical work in structured environments are currently most at risk of being replaced by robots, while administrative and document-processing tasks could be replaced by AI. There has been growing concern that AI and robots will replace people in jobs as technology advances. A report in May from workforce consulting firm Challenger, Gray and Christmas found that US companies have laid off an estimated 49,135 people in 2026 due to AI. However, Obst said that humanoid robots are unlikely to see a mass rollout soon because they don't appear to be more efficient or less error-prone than current robotic manufacturing methods. "Even in relatively structured settings, they still face problems with reliability, speed, safety, cost, and recovery from unexpected situations," he said. "The harder the environment is to control, the harder the robotics problem becomes. Most human jobs involve more variation and more judgment than the package-sorting demonstration." "I would not say we are at the point of mass replacement by humanoid robots. We are much closer to the selective automation of some tasks. AI software is moving faster and is already affecting some forms of information work, but physical robots still have a much harder problem to solve." In another video in May, a human worker managed to sort more packages compared to a team of Figure's robots, which swapped out when needing a recharge. Figure CEO Brett Adock said it would be the last time "a human will ever win." Congrats to Aime!! He said his left forearm is basically broken. Final scores: F.03: 12,732 packages (2.83 seconds/package) - Aime: 12,924 packages (2.79 seconds/package). This is the last time a human will ever win. Markus Levin, co-founder of decentralized data network XYO, said AI models and automation software can perform repetitive tasks with far greater consistency and endurance than humans; however, robots still require charging, maintenance and supervision. A report in September from the International Federation of Robotics found that global demand for factory robots has doubled over the last decade, with warehouses and logistics among the fastest-growing areas of adoption. "I believe broad human replacement is still likely years away," Levin added, "Reliability, safety, regulation, infrastructure costs, and trust remain major barriers to full-scale deployment across society. The challenge is no longer simply making machines capable of acting but ensuring they can operate safely and reliably as they take on greater autonomy." Dr Francisco Cruz Naranjo, a senior lecturer at the University of New South Wales with a PhD in robotics, said the efficiency of robots compared to people depends heavily on the activity and the environment. "Robots are much better at repetitive tasks without the need for constant pauses, as showcased in the Figure livestream. However, in highly dynamic environments, robots still struggle to quickly adapt to changing conditions," he said. "Humans, in this case, are much better. This is precisely why robots at the moment are highly efficient in controlled environments, such as factories, but they have not yet succeeded widely in home settings." Naranjo said repetitive jobs performed in a less static setting are at risk of being replaced by robots, but it will depend on how quickly research advances and how quickly society adapts in areas like making spaces robot-friendly, which is likely years away. Naranjo and Obst said that a mass rollout of robots in the workforce could be of some benefit, such as improving work-life balance, increasing the workforce in areas with shortages, and addressing dangerous environments that are too risky for humans. "The social question is harder. If robots make dangerous work cheaper in human terms, that can be good. But it can also have unintended consequences. For example, keeping humans out of harm's way in military operations may save lives, but it could also lower the perceived cost of conflict," Obst said. "Hypothetically, if we became very successful at automating almost all work, then society would need to rethink economies that are currently built around individual wages and employment." 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Geeky Gadgets The Latest Technology News 7:00 am May 30, 2026 By Julian Horsey Google’s decision to sell Boston Dynamics in 2017 underscored the tension between research-driven robotics and the demands of commercial viability. As Chromeborne explains, the sale stemmed from a mismatch between Boston Dynamics’ emphasis on experimental advancements, such as the humanoid robot Atlas and Google’s focus on creating products with clear and immediate market applications. This case illustrates the broader challenge of balancing long-term technological exploration with the pressures of short-term business goals. Explore how Boston Dynamics shifted its priorities under new ownership, including its move toward warehouse automation and logistics. Gain insight into the roles played by SoftBank and Hyundai in shaping the company’s trajectory. Understand the broader implications of integrating advanced robotics into industries that demand both innovation and profitability. TL;DR Key Takeaways : Boston Dynamics was established in 1992 by Marc Raibert, a visionary in the field of robotics. His ambition was to create machines capable of mimicking the agility, balance and movement of animals. From its inception, the company concentrated on dynamic locomotion, pushing the boundaries of what robots could achieve. Early projects were heavily research-driven, often funded by organizations like the Defense Advanced Research Projects Agency (DARPA). These initiatives prioritized technological breakthroughs over immediate commercial applications, solidifying Boston Dynamics’ reputation as a leader in innovation. The company’s early work laid the groundwork for its future success. By focusing on solving complex problems in robotic movement, Boston Dynamics developed technologies that would later influence the broader robotics industry. However, this emphasis on research over practicality also posed challenges, particularly when it came to finding real-world applications for their innovations. Boston Dynamics achieved several key breakthroughs that demonstrated the potential of advanced robotics. These milestones not only showcased the company’s technical expertise but also highlighted the challenges of translating innovation into practical use: While these innovations were new, their lack of immediate, practical applications hindered their commercial potential. This gap between technological achievement and market readiness became a recurring challenge for Boston Dynamics. Expand your understanding of Boston Dynamics with additional resources from our extensive library of articles. In 2013, Google acquired Boston Dynamics as part of its broader exploration into robotics and automation. At the time, Google was investing heavily in emerging technologies, aiming to position itself as a leader in the field. However, the partnership quickly revealed a fundamental misalignment of priorities. Google sought to develop robots that could address immediate industrial needs, focusing on market-ready solutions. In contrast, Boston Dynamics remained committed to long-term innovation and experimental prototypes. This divergence in goals created tension between the two companies. Boston Dynamics’ research-driven approach did not align with Google’s commercial ambitions, leading to friction and ultimately the decision to sell the company in 2017. The sale highlighted the challenges of integrating a research-focused organization into a commercially driven enterprise. When SoftBank acquired Boston Dynamics in 2017, the company began to pivot toward practical, real-world applications. This shift was evident in the development of quieter, electric-powered robots like Spot, which was designed to perform tasks in various industries. Spot’s capabilities included: Spot’s versatility and ability to navigate complex environments made it a valuable tool for industries such as construction, energy and manufacturing. Additionally, Boston Dynamics’ acquisition of Kinema Systems, a company specializing in robotic vision, enhanced its robots’ autonomy and adaptability, further aligning the company with market demands. In 2021, Hyundai acquired Boston Dynamics, marking another significant turning point in the company’s evolution. With Hyundai’s backing, Boston Dynamics intensified its focus on aligning its innovations with industrial needs. This partnership emphasized the development of robots for factory and warehouse automation, areas where robotics could deliver immediate value. Under Hyundai’s ownership, Boston Dynamics expanded the applications of its robots. Spot became a reliable tool for inspection tasks, while Atlas demonstrated potential for performing repetitive labor in controlled environments. This shift toward practical, real-world uses allowed Boston Dynamics to transition from a research-focused organization to a commercially viable enterprise. Hyundai’s support also provided the resources needed to scale production and refine the company’s technologies for broader adoption. Boston Dynamics faced numerous challenges throughout its journey, including: The company addressed these challenges by focusing on autonomy, environmental awareness and electric-powered designs. By adapting its technologies to meet market demands, Boston Dynamics successfully bridged the gap between innovation and practicality. This approach not only ensured the company’s survival but also solidified its position as a leader in the robotics industry. Today, Boston Dynamics is recognized as a pioneer in robotics, producing machines with clear, defined purposes. Spot has become a staple in industrial inspections, while Atlas continues to evolve as a platform for repetitive tasks in controlled environments. The company’s transformation from a research-driven organization to a commercially focused business underscores its ability to adapt and thrive in a competitive and rapidly evolving industry. As robotics technology continues to advance, Boston Dynamics remains at the forefront, shaping the future of automation and dynamic locomotion. Its journey serves as a powerful example of how innovation, when balanced with practicality, can drive progress and redefine what is possible in the field of robotics. Media Credit: Chromeborne Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

Tinah F. Publié le 15 mai 2026 Mis à jour le 19 mai 2026 2 minutes de lecture Robotique Le robot Figure 03 de Figure AI vient de réaliser une démonstration qui fait parler dans le monde de la robotique. Il a effectué plus de 40 heures de tri de colis autonome sans pause ni assistance humaine. Ce qui est impressionnant dans cette histoire, ce n’est pas seulement qu’un robot humanoïde sache déplacer des colis sans tout faire tomber au bout de trois minutes. Le vrai sujet, c’est l’autonomie. Le robot Figure 03 aurait travaillé plus de 40 heures d’affilée sans interruption, sans assistance humaine et sans pause improvisée devant une machine à café inexistante. Figure AI veut donc prouver l’endurance de ses machines. Derrière cette démonstration, il y a surtout Helix-02, le nouveau réseau neuronal développé par Figure AI. C’est lui qui pilote les capacités des robots Figure 03 pendant ces longues sessions de travail. Le point mis en avant par l’entreprise n’est pas seulement la précision des mouvements. Figure insiste surtout sur la continuité du service. Les robots peuvent détecter certaines erreurs et reprendre automatiquement une tâche interrompue. Ils seraient aussi capables de gérer le remplacement de leurs batteries grâce à plusieurs unités fonctionnant en relais. ⚡️ INSIGHT: Figure says its robot crossed 30 hours of continuous autonomous work with no downtime. pic.twitter.com/4uAcPmoUYV Autrement dit, l’objectif n’est plus simplement de fabriquer un robot qui sait déplacer une boîte. Le vrai défi consiste maintenant à maintenir un système autonome pendant des dizaines d’heures dans un environnement industriel réel. Et c’est précisément là que les choses deviennent intéressantes. Parce qu’entre une vidéo virale sur X et une chaîne logistique qui tourne jour comme de nuit, il y a un gouffre technique et financier. Figure AI affirme également avoir expédié 350 robots depuis son usine BotQ de Sunnyvale, avec un rythme d’environ un robot produit par heure. Ces chiffres montrent surtout l’ambition industrielle de la société. Figure AI commence à se faire de la place dans le domaine de la robotique. Fondée seulement en 2022 par Brett Adcock, la société s’est rapidement imposée dans une course dominée par des géants comme Tesla ou Boston Dynamics. L’arme principale de Figure AI, c’est évidemment le robot Figure 03. Cet humanoïde de troisième génération représente une énorme évolution par rapport aux anciens prototypes de la marque. Beaucoup de robots humanoïdes restent encore limités à des démonstrations très contrôlées. De son côté, Figure 03 cherche surtout à prouver qu’il peut fonctionner dans des environnements réels. Et l’entreprise multiplie les démonstrations pour le montrer. Ces derniers mois, Figure AI a notamment diffusé une vidéo où l’on voit le robot ranger une chambre, déplacer des objets et organiser l’espace avec des gestes étonnamment fluides. 28 mai 2026 26 mai 2026 25 mai 2026 Rejoignez nos 100 000 passionnés et experts et recevez en avant-première les dernières tendances de l’intelligence artificielle🔥 Accueil > Robotique > Vous vous souvenez du robot Figure 03 ? Il travaille maintenant 40 heures d’affilée Rejoignez nos 100 000 passionnés et experts et recevez en avant-première les dernières tendances de l’intelligence artificielle🔥 Rejoins nos 100 000 passionnés et experts et reçois en avant-première les dernières tendances de l’intelligence artificielle🔥

Figure ha mostrato un nuovo video che ha per protagonista un "attore" nascosto, Helix 02, il sistema di controllo basato su una policy Vision-Language-Action che guida i suoi robot umanoidi. Nel filmato due robot F.03 sistemano una camera da letto in meno di due minuti. La scena che ha attirato più attenzione è quella del letto: i due umanoidi si mettono ai lati opposti, sollevano e stendono il piumone, correggono le pieghe e lavorano sullo stesso oggetto deformabile. Secondo Figure, i due robot usano una sola rete appresa, capace di tradurre immagini e istruzioni in movimenti. Non ci sarebbe un pianificatore condiviso, né scambio di messaggi o regia centrale. Ogni umanoide osserva la stanza dalle proprie telecamere e deduce le intenzioni dell'altro dai movimenti. Vai all'approfondimento La sequenza include anche altre azioni. Attraverso gli arti dei due robot, Helix 02 apre porte, appende un indumento, mette via cuffie su un supporto, chiude un libro, getta un rifiuto usando il pedale del cestino e spinge una sedia sotto la scrivania. Servono camminata, equilibrio, mani e lettura continua dell'ambiente. La collaborazione intorno al letto resta il passaggio più interessante. Un piumone non ha una posa fissa, si piega, scivola e cambia forma dopo ogni tiro. Quando un robot modifica la tensione del tessuto, anche il compito dell'altro cambia nello stesso istante. Come sempre in questi casi, la demo però mostra una camera specifica e non chiarisce quante prove siano state necessarie, o quanto cambi l’esito con oggetti disposti diversamente o con arredi non presenti nei dati di addestramento. Cioè, in sostanza, se i robot F.03 con Helix 02 riuscirebbero a destreggiarsi in qualsiasi altro ambiente. © riproduzione riservata Copyright © 2026 DDay.it - Scripta Manent servizi editoriali srl - Tutti i diritti sono riservati - P.IVA 11967100154

Figure AI pubblica un aggiornamento cruciale sulla rampa di produzione del suo robot umanoide di terza generazione, il F.03, nello stabilimento BotQ. Il video, caricato il 29 aprile 2026, dura circa 2 minuti e 53 secondi e mostra le impressionanti conquiste industriali raggiunte dall’azienda. In soli 120 giorni, Figure ha scalato la produzione di 24 volte, passando da 1 robot al giorno a 1 robot all’ora. Questa crescita esponenziale dimostra la maturità del processo manifatturiero, passando da prototipi artigianali a una linea di produzione automatizzata e scalabile. Figure AI, fondata nel 2022 da Brett Adcock e con sede a Sunnyvale in California, si posiziona come leader nella robotica AI per creare umanoidi autonomi commercialmente viabili. BotQ rappresenta l’impianto manifatturiero dedicato di Figure, progettato per alte volumi senza dipendere da fornitori esterni, garantendo controllo su qualità e iterazioni rapide. Questa settimana, l’azienda produrrà 55 robot F.03, confermando l’obiettivo di 12.000 unità annue sulla prima linea, con un target di 100.000 robot in quattro anni. La transizione a processi come fusione sotto pressione, stampaggio a iniezione e timbratura ha ridotto drasticamente i costi unitari, rendendo il F.03 pronto per la scala globale. Il F.03 è stato ridisegnato da zero per Helix, il modello vision-language-action di Figure, integrando percezione, ragionamento e controllo in un unico cervello generalista. Con un design più morbido e testurizzato, mani dexterous a 20 gradi di libertà e batteria integrata nel torso con densità energetica aumentata del 94% rispetto alle generazioni precedenti, il robot è ottimizzato per compiti domestici come lavare piatti o pulire. Figure mira a portare questi umanoidi in ogni casa, espandendo le capacità umane attraverso AI avanzata e produzione di massa. Sfruttando offload dati ad alta velocità, flotte di F.03 possono caricare terabyte per apprendimento continuo, rivoluzionando fabbriche e residenze. Il video non è solo un annuncio, ma una vetrina sul progresso di Figure verso la produzione seriale, evidenziando l’impegno per supply chain interne e innovazione hardware. Rilasciato in un momento di accelerazione manifatturiera, rafforza la posizione di Figure nel panorama competitivo della robotica, con focus su sicurezza, costo-efficacia e autonomia reale. Questa milestone a BotQ segnala che i robot umanoidi non sono più prototipi da laboratorio, ma prodotti pronti per il deployment massivo. Figure AI sta trasformando l’industria robotica, puntando a umanoidi versatili per case e fabbriche entro i prossimi anni. Il tuo indirizzo email non sarà pubblicato. I campi obbligatori sono contrassegnati * Commento * Nome * Email * Sito web Salva il mio nome, email e sito web in questo browser per la prossima volta che commento. Il video è la registrazione del keynote tenuto da Jensen Huang, fondatore e CEO di NVIDIA, all’NVIDIA GTC Taipei 2026, svoltosi in coincidenza con il COMPUTEX di Taipei il 31 maggio 2026 presso il Taipei Music Center. Si tratta di uno degli eventi tecnologici più attesi dell’anno, nel corso del quale Huang ha annunciato alcune […] Il video pubblicato dall’archivio della BBC è un affascinante documento storico tratto dalla trasmissione Micro Live, originariamente andata in onda su BBC Two il 12 dicembre 1986. In poco più di sei minuti, la giornalista Lesley Judd compie un viaggio dall’Inghilterra all’aeroporto di Schiphol, in Olanda, per dimostrare al pubblico televisivo dell’epoca come fosse possibile […] Il video pubblicato da OpenAI il 22 maggio 2026 presenta il lancio in preview di una nuova esperienza di finanza personale integrata in ChatGPT, disponibile per gli utenti Pro negli Stati Uniti. Connessione sicura dei conti finanziari Gli utenti possono ora collegare in modo sicuro i propri conti bancari e finanziari a ChatGPT attraverso Plaid, […] Google I/O 2026 viene presentato come il momento in cui l’azienda fa un salto netto verso una “era agentica”, in cui l’intelligenza artificiale non si limita a rispondere ma pianifica, decide e agisce nel mondo digitale degli utenti.Il keynote mette al centro Gemini come piattaforma unificata che alimenta prodotti, servizi e dispositivi, con l’obiettivo dichiarato […] Cos’è Googlebook Googlebook è il nuovo nome di una categoria di laptop che Google ha presentato recentemente nell’ambito dell’Android Show 2026, posizionandolo come il primo portatile progettato da cima a fondo per Gemini Intelligence. 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Questa build, durata due anni, fonde il fascino vintage della muscle car americana con le tecnologie all’avanguardia di Tesla, creando un’auto unica che mantiene l’estetica […] Ubuntu 26.04 LTS, nome in codice Resolute Raccoon, è stata rilasciata il 23 aprile 2026 da Canonical come undicesima release long-term support di Ubuntu. È pensata per un uso professionale e di produzione, con un forte accento su affidabilità, sicurezza e supporto ai carichi di lavoro moderni, in particolare nell’ambito dell’intelligenza artificiale e dell’infrastruttura cloud. […] Figure AI pubblica un aggiornamento cruciale sulla rampa di produzione del suo robot umanoide di terza generazione, il F.03, nello stabilimento BotQ. Il video, caricato il 29 aprile 2026, dura circa 2 minuti e 53 secondi e mostra le impressionanti conquiste industriali raggiunte dall’azienda. In soli 120 giorni, Figure ha scalato la produzione di 24 […] Brick Machines ha realizzato una LEGO Coffee Factory capace di preparare il caffè su comando tramite smartphone, trasformando una semplice costruzione in una macchina funzionante e scenografica. Il progetto unisce creatività, automazione e spirito da laboratorio, con un risultato pensato sia per stupire sia per essere davvero utile. Come funziona la macchina La struttura utilizza […] Il video racconta Claude Design come una piattaforma in cui l’utente non si limita a descrivere un’idea, ma la trasforma in un progetto visivo vero e proprio attraverso una conversazione con Claude. Il messaggio più importante è che la progettazione viene resa più immediata, perché l’intelligenza artificiale costruisce una prima versione del lavoro e poi […] Il video “Miniature Mountain Magic: A Tilt-Shift Journey through Four Seasons in the Alps” mostra le Alpi bavaresi in una chiave visiva giocosa e spettacolare, trasformando il paesaggio in un mondo che sembra in miniatura. È un lavoro di Joerg Daiber per il progetto Little Big World, costruito con riprese aeree, time-lapse e tecnica tilt-shift. […] VOXmail è una piattaforma italiana dedicata all'invio di newsletter e all'email marketing, attiva dal 2008 e sviluppata interamente in Italia da Void Labs. Ogni giorno, migliaia di utenti si affidano a questo servizio per raggiungere i propri iscritti con comunicazioni efficaci e profession [...] FlowSpeech è una piattaforma di sintesi vocale basata sull'intelligenza artificiale che trasforma testi, documenti e immagini in audio professionale dall'intonazione naturale, con controllo avanzato delle emozioni, delle pause e dello stile narrativo. Che cos'è FlowSpeech e Come Funzion [...] PicPocket è un’app pensata per condividere e organizzare foto e video con amici, famiglia e gruppi in modo semplice, veloce e ordinato (mette a disposizione spazio per 2000 foto gratis) Sostituisce allegati email e link complicati con uno spazio condiviso che assomiglia a una chat, ma co [...] Cherri (pronunciato "cherry") è un linguaggio di programmazione open source dedicato a Siri Shortcuts, progettato per compilare direttamente uno Shortcut valido e firmato, pronto per essere eseguito su tutti i dispositivi Apple. Che Cos'è Cherri e Qual È il Suo Obiettivo Principale [...] Questo Sfondo dedicato al logo Apple per il WWDC26. Ottimo per iphone ha una risoluzione di 1206 x 2672.. E' appartenente alla categoria Tecnologia ed è reperibile all'interno dei wallpapers della pagina Apple Download Wallpaper L'immagine è ottimizzata per essere utilizzata su smartphone [...] Il video è la registrazione del keynote tenuto da Jensen Huang, fondatore e CEO di NVIDIA, all'NVIDIA GTC Taipei 2026, svoltosi in coincidenza con il COMPUTEX di Taipei il 31 maggio 2026 presso il Taipei Music Center. Si tratta di uno degli eventi tecnologici più attesi dell'anno, nel corso de [...]

Quando Figure AI ha pubblicato il video Helix-02 Bedroom Tidy, mostrando due robot umanoidi Figure 03 rifare il letto e sistemare i vestiti in una camera, l’industria della robotica ha alzato le antenne. Ma mentre tutti guardavano a ovest, dall’altra parte del mondo stava emergendo con discrezione un protagonista cinese capace di fare molto di più che girare video dimostrativi: OneRobotics. Fondata nel 2015 a Shenzhen da due laureati dell’Harbin Institute of Technology, la società è già nota a milioni di persone per la sua linea SwitchBot di dispositivi per la domotica, tra cui apricurtine intelligenti e serrature connesse. Negli ultimi anni, però, ha compiuto un salto qualitativo significativo, espandendosi nella robotica domestica basata su intelligenza artificiale con un’architettura proprietaria chiamata “One Brain, Multiple Embodiments”, il cui cuore è il modello AI OneModel, capace di condividere capacità e continuare ad evolversi tra diverse tipologie di robot. I numeri raccontano una crescita solida: il fatturato principale è passato da 275 milioni di yuan nel 2022 a 610 milioni di yuan nel 2024, con il Giappone che ha rappresentato il 68% dei ricavi nel primo semestre 2025. Non a caso, la televisione pubblica giapponese NHK ha dedicato un’intervista speciale all’azienda poco dopo la pubblicazione del video di Figure AI, concentrandosi proprio sulla dimostrazione live del robot onero H1 che, in un ambiente domestico reale, ha eseguito il flusso completo di riconoscimento degli indumenti, presa e inserimento nel cestello della lavatrice, senza scenografie costruite ad arte. Il 30 dicembre 2025, OneRobotics è approdata sul Main Board della Borsa di Hong Kong con il codice 06600.HK, diventando la prima società al mondo quotata in borsa focalizzata sui robot domestici con intelligenza artificiale embodied. L’IPO ha raccolto circa 1,64 miliardi di dollari di Hong Kong (circa 188 milioni di euro), e la capitalizzazione di mercato, già a inizio gennaio 2026, aveva superato i 23 miliardi di HKD, pari a circa 2,7 miliardi di euro. Seleziona SmartDomotica.it come fonte preferita su Google L’approccio “una sola mente, molteplici forme” si traduce concretamente in tre linee di prodotto che coprono altrettanti scenari domestici fondamentali. Il robot compagno Kata Friends si rivolge all’interazione e alla compagnia intelligente, Acemate entra nell’ambito dello sport e del benessere, mentre onero H1 affronta i servizi domestici veri e propri. Tutti condividono lo stesso cervello artificiale, con il vantaggio che ogni dato raccolto in un contesto arricchisce l’intera piattaforma. Acemate è già diventato un caso a sé: classificato come il primo robot da tennis con AI al mondo, integra visione artificiale, interazione ad alta dinamica e decision-making in tempo reale per muoversi autonomamente e rispondere ai colpi. Questa innovazione gli ha valso un posto nella celebre lista delle migliori invenzioni 2025 del Time, lo stesso riconoscimento che ha portato anche Figure 03 sotto i riflettori. Una coincidenza che dice molto sulla direzione in cui si sta muovendo l’industria. Sul fronte delle infrastrutture dati, a inizio 2026 OneRobotics ha vinto una gara pubblica a Shenzhen per la costruzione di un “Embodied Intelligence Data Full-Chain Service Center”, un contratto dal valore di 44,95 milioni di yuan (circa 5,6 milioni di euro). Il progetto prevede il dispiegamento di unità onero H1 a doppio braccio mobile, terminali di acquisizione dati UMI e sistemi indossabili per la teleoperazione, con scenari applicativi che spaziano dall’assistenza agli anziani al retail, fino alla ricerca scientifica. Si tratta di un passaggio che segna l’evoluzione dell’azienda da produttore di robot commerciali a vero e proprio fornitore di infrastrutture per la raccolta e l’addestramento dei dati, chiudendo il cerchio tra corpi fisici, scenari reali e miglioramento continuo dei modelli. Con prodotti distribuiti in oltre 90 Paesi e regioni e più di 3,6 milioni di famiglie già servite nel mondo, OneRobotics non parte da zero sul fronte della penetrazione commerciale. E proprio qui risiede forse il vantaggio competitivo più difficile da replicare: non la capacità di girare video spettacolari in ambienti controllati, ma quella di accumulare dati reali da case reali, dove le variabili sono infinite e la complessità non si può simulare in laboratorio. Seleziona SmartDomotica.it come fonte preferita su Google

Figure AI ha recentemente svelato un avanzato sistema di controllo chiamato Helix 02, progettato per gestire i suoi robot umanoidi. Questo sistema è in grado di guidare i robot senza la necessità di un pianificatore centralizzato, permettendo così una collaborazione fluida e autonoma, come dimostrato in un video che ha catturato l’attenzione del pubblico. Nel video, due robot F.03 sono stati filmati mentre sistemano una camera da letto in meno di due minuti. La scena più impressionante è quella in cui i robot collaborano per sistemare un piumone. Entrambi si posizionano ai lati opposti del letto, sollevano e stendono il piumone, correggendo le pieghe e adattandosi ai movimenti reciproci. Questa azione evidenzia non solo la loro capacità di lavorare insieme, ma anche l’efficacia del sistema Helix 02 nel tradurre istruzioni visive in movimenti coordinati. Una delle caratteristiche distintive di Helix 02 è l’assenza di una regia centrale o di uno scambio di messaggi tra i robot. Ognuno di essi utilizza le proprie telecamere per osservare l’ambiente e dedurre le intenzioni dell’altro in base ai movimenti. Questo approccio decentralizzato consente ai robot di adattarsi rapidamente ai cambiamenti nell’ambiente, rendendoli più versatili e reattivi. Il sistema Helix 02 integra una “memoria muscolare”, che consente ai robot di apprendere e migliorare le proprie abilità nel tempo. Nel video, i robot non solo sistemano il piumone, ma eseguono anche una serie di altre azioni, come aprire porte, appendere indumenti e gettare rifiuti. Tuttavia, la demo solleva interrogativi sulla versatilità di questi robot. Non è chiaro quante prove siano state necessarie per raggiungere tali risultati e come si comporterebbero in ambienti con arredi o oggetti non presenti durante l’addestramento. La tecnologia sviluppata da Figure AI rappresenta un passo significativo verso il futuro della robotica. La capacità di lavorare in modo autonomo e collaborativo potrebbe avere applicazioni in vari settori, dall’assistenza domestica alla logistica. Tuttavia, è fondamentale continuare a esplorare le possibilità e i limiti di questi sistemi per comprendere appieno il loro potenziale e le sfide che si presenteranno. © 2003 - 2025 Mr. Webmaster ® è un marchio registrato.E' vietata ogni forma di riproduzione. Un progetto a cura di IKIweb Internet Media S.r.l. - P.IVA: 02848390122 - NREA: VA-294824 - Cap. soc. 10.000 Eu i.v. - Sede legale: Via Varzi 6, Busto A. (VA) - Sede operativa: Vicolo dell'Assunta 5, Busto A. (VA) Gestisci le preferenze pubblicitarie

Décidément, l’heure est à la phase de la production de masse pour les robots humanoïdes. La firme américaine Figure AI franchit une étape industrielle majeure en annonçant avoir multiplié par 24 son rythme de production en moins de quatre mois, passant d’un robot par jour à un robot par heure pour son modèle Figure 03 ! Cette montée en cadence s’appuie sur BotQ, l’usine conçue par Figure AI pour fabriquer ses humanoïdes à plus grande échelle. L’entreprise indique avoir déjà produit plus de 350 robots Figure 03 et démontré le cycle nécessaire à ses objectifs de production : « Nous avons réussi à démontrer le temps de cycle d’un robot par heure nécessaire à nos objectifs de production du Figure 03. »  Pour y parvenir, Figure AI a mis en place des lignes dédiées aux modules critiques du robot, pilotées par un logiciel interne de gestion de production. Plus de 150 postes de travail connectés seraient désormais utilisés dans cette structure industrielle. Outre cette production de masse, Figure AI dispose aussi d’une unité dédiée à l’entrainement des robots située dans son quartier général : dans ce lieu unique, on peut voir des robots déambuler dans les couloirs ou répéter des gestes précis, le tout donnant l’impression d’être littéralement projeté dans une version réelle de la série Westworld (voir vidéo ci-dessous). La production de centaines de robots permet ainsi Figure AI d’accumuler davantage de données terrain (et plus rapidement) pour améliorer Helix, son système d’intelligence artificielle destiné à piloter les tâches physiques du quotidien. La société affirme aussi avoir dépassé les 9 000 actionneurs produits, avec plus de dix variantes de composants. Figure AI espère ainsi réduire les coûts, fiabiliser les machines et préparer leur déploiement dans les entreprises… avant d’envisager la vente aux particuliers. Signaler une erreur dans le texte Merci de nous avoir signalé l'erreur, nous allons corriger cela rapidement. Δ Nous nous réservons le droit de supprimer les commentaires qui ne respectent pas ces règles Meta a annoncé aujourd’hui de nouveaux garde-fous sur Instagram, Facebook et Messenger pour limiter l’exposition... SFR annonce la disponibilité de « SFR Navigation Protégée », une nouvelle protection de... Anthropic a déposé son projet d’introduction en Bourse, ouvrant une nouvelle phase pour l’un des groupes les plus suivis de... Le premier indice de qualité de MoffettNathanson bouscule la hiérarchie habituelle du streaming en plaçant Apple TV devant Netflix.... Amazon annonce les dates pour le Prime Day 2026, son événement shopping avec plein de réductions : ce sera du 23 juin à... Météo Météo Musique Musique Jeux Musique Musique Divertissement Utilitaires Jeux Thriller Thriller Horreur Thriller Thriller Drame Action et aventure Comédie 2 Jun. 2026 • 16:00 2 Jun. 2026 • 15:11 2 Jun. 2026 • 13:33 2 Jun. 2026 • 11:26 Actualité High-Tech, Culture Geek et comparateur de prix Recherchez le meilleur prix des produits Hi-tech Recherchez des articles sur le site

Figure AI ha messo un robot umanoide contro un lavoratore reale in una sfida di 10 ore. Il risultato finale mostra quanto la distanza si sia ridotta. Pubblicato: 18 Maggio 2026 Biagio Petronaci Tech Editor Scrive di tecnologia e innovazione digitale analizzando trend e impatti socio-culturali. Per anni, il racconto sull’automazione industriale è stato accompagnato dalla stessa idea: i robot avrebbero superato rapidamente gli esseri umani nei lavori ripetitivi. La prova organizzata da Figure AI restituisce invece uno scenario più complesso. Dopo dieci ore consecutive di lavoro in magazzino, il robot umanoide F.03 non è riuscito a battere un dipendente umano in una gara di smistamento pacchi. Il distacco finale è stato minimo, ma sufficiente a lasciare un dato concreto: almeno oggi, nelle attività fisiche reali, l’uomo conserva ancora un vantaggio. La sfida è stata trasmessa in diretta streaming e ha attirato molta attenzione online, anche perché rappresenta uno dei test pubblici più chiari sul livello raggiunto dalla robotica umanoide applicata alla logistica. Il test prevedeva un compito semplice da descrivere, ma difficile da sostenere per ore senza rallentamenti. Il robot e il concorrente umano dovevano individuare il pacco, riconoscere il codice a barre, afferrarlo e posizionarlo correttamente su un nastro trasportatore con il barcode rivolto verso il basso. Da una parte c’era il robot F.03 di Figure AI, dall’altra Aime, intern dell’azienda. Entrambi hanno lavorato per dieci ore consecutive mantenendo la stessa routine operativa. Alla fine della prova, il lavoratore umano ha elaborato 12.924 pacchi contro i 12.732 completati dal robot. Anche la differenza media per singola operazione è stata minima: 2,79 secondi per pacco contro 2,83. Il dato più interessante è proprio questo. Non si tratta più di una macchina lontana dalle prestazioni umane, ma di un sistema ormai abbastanza vicino da rendere il confronto credibile anche in un contesto operativo reale. Durante la competizione, Aime ha effettuato la pausa pranzo e i momenti di riposo previsti dalle normative sul lavoro. Il robot, invece, ha continuato a operare senza interruzioni. Secondo quanto dichiarato dal CEO Brett Adcock, il dipendente avrebbe concluso la giornata con un forte affaticamento fisico e problemi all’avambraccio dopo ore di movimenti ripetuti. È un elemento che cambia il significato del risultato finale. In una singola giornata, il vantaggio umano è rimasto intatto, ma la resistenza sul lungo periodo continua a essere uno dei principali punti di forza delle macchine. Figure AI sostiene infatti che i propri sistemi siano in grado di lavorare per turni molto più lunghi senza cali fisici evidenti. Nei test pubblicati dalla società, i robot avrebbero continuato a smistare pacchi anche oltre la durata della sfida pubblica. La logistica resta uno dei principali obiettivi della robotica umanoide proprio per la natura ripetitiva delle attività di magazzino. Figure AI punta a dimostrare che i suoi robot possano lavorare accanto agli esseri umani, ma secondo alcuni esperti la tecnologia non sarebbe ancora pronta per un’adozione industriale davvero ampia e stabile. La sfida organizzata da Figure AI mette in evidenza un paradosso sempre più evidente. Mentre molte aziende sostengono che l’intelligenza artificiale automatizzerà rapidamente i lavori d’ufficio, i robot umanoidi non riescono ancora a superare gli esseri umani nelle attività fisiche reali. Nonostante i progressi della robotica, il vantaggio umano nei contesti operativi concreti resta tangibile. Allo stesso tempo, però, il risultato ottenuto da F.03 rimane significativo proprio per la distanza ormai ridotta rispetto alle prestazioni di un lavoratore umano. FAQ Chi ha vinto la sfida tra F.03 e l'umano? L'intern Aime ha smistato 12.924 pacchi contro 12.732 del robot F.03, risultando vincitore nella giornata. Qual era il compito della prova? Individuare il pacco, leggere il codice a barre, afferrarlo e posizionarlo con il barcode rivolto verso il basso. Quanto era la differenza media per pacco? La differenza media per singola operazione è stata minima: 2,79 s per l'umano contro 2,83 s per il robot. Il robot ha fatto pause durante la prova? No, il robot ha operato senza interruzioni mentre l'umano ha osservato le pause previste dalle normative. Perché i magazzini sono adatti ai robot umanoidi? La logistica implica attività ripetitive e turni lunghi, dove la resistenza e la continuità delle macchine sono un vantaggio. L'intern Aime ha smistato 12.924 pacchi contro 12.732 del robot F.03, risultando vincitore nella giornata. Individuare il pacco, leggere il codice a barre, afferrarlo e posizionarlo con il barcode rivolto verso il basso. La differenza media per singola operazione è stata minima: 2,79 s per l'umano contro 2,83 s per il robot. No, il robot ha operato senza interruzioni mentre l'umano ha osservato le pause previste dalle normative. La logistica implica attività ripetitive e turni lunghi, dove la resistenza e la continuità delle macchine sono un vantaggio. Come funzionerà il Custom Feed di YouTube? Samsung, la soundbar top oggi costa pochissimo: va comprata subito Smart TV Panasonic, prezzo mai visto prima: solo oggi costa la metà Galaxy A56, con lo sconto di oggi il prezzo crolla al minimo storico Come potrebbe essere fatta una forma di vita aliena? Ecco qualche ipotesi Amazon anticipa il Prime Day 2026: offerte esclusive e iniziative dedicate al Sud Italia Pop-up "polyfill.io" blocca le smart TV Samsung: cos'è e come risolvere subito Nvidia vuole reinventare il PC: il nuovo chip RTX Spark che sfida Intel e Apple Investimenti, incentivi e strategie per il 2026 © Italiaonline S.p.A. 2026Direzione e coordinamento di Libero Acquisition S.á r.l.P. IVA 03970540963

Teknoloji ve bilim dünyasını seven ve takip etmekten büyük zevk alan Metin, öğrendiklerini ise DonanımHaber okuyucuları ile paylaşır. Tam Boyutta Gör ABD merkezli robotik şirketi Figure AI, BotQ üretim tesisinde insansı robot modeli Figure 03 için üretim hızını günde bir adet seviyesinden saatte bir adet seviyesine çıkararak yalnızca dört ay içinde 24 katlık artış elde etti. Bu gelişme, şirketin prototip aşamasından seri üretime geçtiğini net biçimde ortaya koyuyor. Üretim artışı, şirketin geliştirdiği özel yazılım altyapısı ve 150’den fazla birbirine bağlı iş istasyonundan oluşan üretim hattı sayesinde mümkün oldu. Figure, bu ölçekleme süreciyle birlikte bugüne kadar 350’den fazla robot teslimatı gerçekleştirdiğini açıkladı. Şirketin hedefi ise yıllık 12.000 robot üretim kapasitesine ulaşmak ve bu kapasiteyi daha da yukarı taşımak. Şirket, sadece bu hafta 55 adet insansı robot üreteceğini de açıkladı. Tam Boyutta Gör Figure, üretim verimliliğini artırmak için tedarik zincirinde kalite standartlarını sıkılaştırdı ve üretim sürecine 50’den fazla ara kontrol noktası ekledi. Bu yaklaşımın sonucunda nihai üretim hattında ilk denemede başarı oranı yüzde 80’in üzerine çıktı. Özellikle kritik bileşenlerde dikkat çekici sonuçlar elde edildi. Batarya üretiminde verimlilik yüzde 99,3 seviyesine ulaşırken toplamda 9.000’den fazla aktüatör üretildi. Şirket ayrıca 500’ün üzerinde robotun sevkiyatını tamamladı. Bununla birlikte üretilen her bir robot, üretim sonrası kapsamlı test süreçlerinden geçiriliyor. 80’den fazla fonksiyonel test, erken arızaları önlemek amacıyla uygulanıyor. Bu testler arasında çömelme ve koşu gibi fiziksel dayanıklılık senaryoları da yer alıyor. Şirketin robot filosu büyüdükçe robotlarda kullanılan yapay zeka modeli Helix için de daha fazla ölçekli veri toplanmış oluyor. Bu da robotların otonom yeteneklerinin geliştirilmesine doğrudan katkı sağlıyor. Robotlarla veri merkezi inşa edecek şirket geliyor 1 ay önce eklendi Ayrıca Figure, robot filosunu yönetmek için uzaktan güncelleme (OTA), servis ve filo yönetim sistemleri geliştirdi. Bu sistemler sayesinde sahadaki robotlar sürekli izlenebiliyor, güncellenebiliyor ve elde edilen geri bildirimler doğrudan geliştirme süreçlerine aktarılabiliyor. Şirket, üretim tarafındaki ilerlemelerin yanı sıra yapay zeka modelinde de önemli bir güncelleme duyurdu. Helix System 0 (S0) adlı modelin yeni versiyonu, robotlara çevreyi algılayarak hareket etme yeteneği kazandırıyor. Önceki sürüm yalnızca robotun kendi eklem hareketlerini ve pozisyonunu algılayan proprioseptif verilere dayanıyordu. Bu durum, merdiven veya engebeli zemin gibi karmaşık ortamlarda hareket kabiliyetini sınırlıyordu. Yeni güncellemeyle birlikte robotlar artık stereo kameralar aracılığıyla elde edilen görsel verileri kullanarak çevrenin üç boyutlu haritasını oluşturabiliyor. Bu sayede robot, bulunduğu ortamı hem “hissedebiliyor” hem de “görebiliyor”. Bu sistemin simülasyon ortamında farklı ve rastgele zemin koşullarında pekiştirmeli öğrenme yöntemiyle uçtan uca eğitildiğinin altı çiziliyor. En dikkat çekici noktalardan biri ise bu öğrenilmiş davranışların, ek bir kalibrasyon gerektirmeden doğrudan gerçek dünyaya aktarılabilmesi oldu. Sonuç olarak Figure 03 robotları artık merdivenleri çıkabilen, farklı zeminlerde dengeli hareket edebilen ve değişken ışık koşullarında stabil performans gösterebilen bir yapıya kavuştu. İnsansı robot Atlas bu kez buzdolabı taşıdı {{Description}} https://www.amazon.com.tr/dp/B0BM62VSHP https://www.amazon.com.tr/dp/B0DT1KLV4H https://www.amazon.com.tr/dp/B0CSNGM6D6 https://www.amazon.com.tr/dp/B09XBTQZF2 https://app.hb.biz/dB52DXc6OZqj https://store.steampowered.com/app/858710/Gravity_Circuit

Published on 01.Jun.2026 in Vol 28 (2026) Authors of this article: 1Department of Educational Sciences, University of Catania, Catania, Sicily, Italy 2Kent and Medway Medical School, University of Kent and Canterbury Christ Church University, Canterbury, England, United Kingdom 3Department of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham, England, United Kingdom 4Department of Computing, Sheffield Hallam University, Sheffield, England, United Kingdom 5School of Health Sciences, University of Southampton, Building 67, Highfield Campus, University Road, Southampton, England, United Kingdom Daniele Magistro, PhD Background: Maintaining cognitive efficiency and independence is a central goal of healthy aging. Socially assistive robots (SARs) are increasingly proposed as scalable digital health solutions to support daily activities in older adults and to facilitate aging-in-place. However, concerns remain regarding whether robot-mediated assistance reduces or inadvertently increases cognitive load, potentially undermining usability, user acceptance, and long-term real-world adoption, particularly in aging populations. Objective: This study aimed to examine how robot-assisted (human-robot interaction [HRI]) and human-assisted (human-human interaction [HHI]) support influences cognitive load during task performance in younger and older adults. A multimodal assessment framework integrating behavioral, subjective, and physiological measures was used to identify age-related differences in cognitive effort and stress associated with different forms of assistance. Methods: A total of 60 healthy adults (30 younger adults: mean age 34.8, SD 10.1 years; and 30 older adults: mean age 72.3, SD 5.5 years) completed a modified Trail Making Test under 7 within-subject conditions: independent performance (baseline), 3 robot-assisted conditions, and 3 human-assisted conditions, each corresponding to low, medium, and high cognitive load levels. Performance accuracy and completion time were recorded as behavioral indicators. Perceived cognitive load was assessed using the National Aeronautics and Space Administration Task Load Index, and physiological stress was evaluated via pre- and postcondition salivary cortisol concentrations. Linear mixed-effects models were applied to examine main effects and interactions of age group, assistance type, cognitive load level, and time. Results: Significant interactions between age group and assistance type were observed for accuracy (F1, 404.53=6.50; P=.01) and perceived cognitive load (F1, 403.45=4.58; P=.03). Older adults demonstrated lower accuracy and higher perceived cognitive load during robot-assisted conditions compared with human-assisted conditions, whereas no such differences were observed in younger adults. Across age groups, human assistance improved performance at low and medium cognitive load levels. Physiological analysis revealed a significant age×assistance× time interaction (F1, 156=5.16; P=.02), with older adults showing increased posttask cortisol concentrations during robot-assisted interaction, indicating higher physiological stress. Conclusions: While both human and robotic assistance enhanced task performance relative to independent completion, the type of support critically shaped cognitive load responses in older adults. Robot-assisted interaction was associated with increased behavioral errors, higher perceived workload, and elevated physiological stress, suggesting that current SAR implementations may impose additional extraneous cognitive load in older users. These findings highlight the importance of designing adaptive, age-sensitive digital assistive systems that minimize cognitive burden through simplified interaction, responsive pacing, and multimodal support. Multimodal cognitive load assessment provides a valuable framework for optimizing the usability and effectiveness of assistive digital health technologies for aging populations. Maintaining independence and cognitive efficiency in daily life constitutes a central objective in the promotion of active and healthy aging [1]. The ability to manage daily activities with adequate support has become a key focus across multiple disciplines, particularly in light of the projected rise in the global mean age over the coming decades [2]. In this context, health and social care systems are increasingly exploring digital health technologies to support autonomy, reduce care burden, and enable aging in place, underscoring the need for scalable and evidence-based interventions aimed at enhancing quality of life among older adults. Technological progress has played a pivotal role in pursuing this goal. Intelligent systems can now be embedded within domestic environments, and embodied agents may be programmed to interact with users to accomplish shared objectives [3]. Within this framework, socially assistive robotics (SAR) leverages human-robot interaction (HRI) to facilitate such activities and to foster autonomy in everyday life [4]. SAR systems are increasingly positioned as interactive digital health interventions, integrating social interaction, guidance, and task support within real-world settings. However, recent evidence indicates that enabling communication with an artificial agent alone is insufficient to ensure effective interaction, particularly in vulnerable groups such as older adults [5]. Beyond demonstrating performance benefits, digital assistive technologies must also be evaluated in terms of usability, acceptability, and sustainability of long-term adoption [6,7]. Interventions that improve task execution but simultaneously increase cognitive or physiological strain may face significant barriers to real-world implementation. From a cognitive ergonomics perspective, cognitive load (CL) represents a critical determinant of HRI effectiveness and of the usability and sustainability of digital health technologies [8]. Conceptually, CL is defined as the amount of mental resources required to perform a given task [9]. Because cognitive resources are inherently limited, individuals must distribute them efficiently across competing activities [10]. Importantly, CL is not a unitary construct but rather comprises three interrelated components: intrinsic, extraneous, and germane load. Intrinsic load stems from the inherent complexity of the task and the volume of information to be processed. Extraneous load arises from the presentation format and the presence of external distractions, both of which can be mitigated through effective task design. Germane load, instead, pertains to the cognitive processes that foster learning and schema construction. Recent theoretical models have proposed integrating intrinsic and germane load, conceptualizing intrinsic and extraneous load as the two principal dimensions of CL theory [11-13]. In applied digital health contexts, extraneous CL introduced by interface design is particularly problematic, as it may negate the intended supportive function of assistive technologies. A growing body of research indicates that older adults face greater challenges in managing CL, thereby affecting their functional autonomy [14]. Such difficulties are often attributed to age-related cognitive decline, which manifests as increased fatigue and reduced efficiency in completing daily tasks [15]. These challenges extend to the use of technology, where the additional cognitive demands associated with complex interfaces further amplify CL [16,17]. Moreover, older adults’ attitudes toward advanced technologies are shaped not only by sociocultural factors but also by individual cognitive needs and the degree of system personalization [18]. Neglecting these factors’ risks imposing additional cognitive burdens and may contribute to digital exclusion among those most in need of support. Despite the growing interest in SAR, few studies have directly compared robot-mediated assistance with human assistance under controlled CL conditions. Such comparisons are essential, as assistive technologies are often implicitly framed as substitutes for human support. Furthermore, assessing technology-assisted performance without an independent baseline condition limits the ability to determine whether observed effects reflect genuine support or task facilitation, as highlighted in previous work [19]. Accordingly, it becomes essential to determine whether technology use increases users’ CL, to identify the features responsible for such increases, and to examine how these effects differ across age groups. Such insights are crucial for the design of adaptive interfaces capable of dynamically accommodating users’ cognitive capacities. Nevertheless, the existing literature presents several methodological limitations in addressing these issues. First, assessing CL during technology use without a baseline condition prevents reliable interpretation. Experimental designs should therefore include a comparison between technology-assisted and independent task performance to delineate the specific benefits and drawbacks of technological aids, a methodological framework exemplified by Varrasi et al [19]. Furthermore, studies asserting the positive impact of technology without incorporating a condition involving human support lack robustness; indeed, multiple aid conditions are required for a comprehensive evaluation. Likewise, hypothesizing user needs without conducting age comparisons constrains the development of genuine person-centered support systems. Specific sampling is thus necessary to enable between-group analyses and to characterize distinct population profiles. Finally, the reliance on a single CL measure limits construct validity, underscoring the need to use multimodal assessment approaches. CL can be quantified through a range of behavioral, subjective, and physiological indicators, each providing complementary information on the cognitive demands placed on the individual. Behavioral indices include performance-based metrics, such as accuracy, error rates, and completion time, which serve as quantifiable markers of cognitive effort [20]. Subjective indices capture self-reported perceptions of mental workload, encompassing aspects such as perceived effort, frustration, and mental demand [21]. Integrating these approaches enables a comprehensive evaluation of cognitive demands and supports the robust assessment of assistive technologies in digital health contexts. Physiological indices, including heart rate variability, pupil dilation, blood and salivary cortisol concentrations, and electroencephalographic (EEG) activity, reflect the body’s physiological responses to cognitive strain and stress [22]. Integrating these methods offers a comprehensive assessment of CL and facilitates a more accurate evaluation of task design and learning efficiency. Given these considerations, this study aimed to investigate whether, and in what ways, HRI and human-human interaction (HHI) influence CL management during task performance in younger and older adults. By incorporating an independent baseline condition and a multimodal psychometric framework, this study seeks to inform the design, evaluation, and deployment of age-sensitive digital assistive technologies. This study used a within-subject, experimental comparative design to examine differences in CL during independent, robot-assisted, and human-assisted task performance in younger and older adults. The study included 60 adult volunteers, comprising 30 younger adults (13 males, 17 females; mean age 34.8, SD 10.1 years) and 30 older adults (12 males, 18 females: mean age 72.3, SD 5.5 years), with sex distribution confirmed as balanced (χ²1=0.07; P=.79). Younger adults were aged 18‐45 years, and older adults were more than 65 years of age. In line with the study aims, age differed significantly between groups (t58=17.86; P<.001). Education levels were broadly comparable across groups. Among younger adults, 16.7% (5/30) reported high school education, 23.3% (7/30) bachelor’s, 30% (9/30) master’s, and 30% (9/30) PhD-level education. Among older adults, 33.3% (10/30) reported high school education, 30% (9/30) bachelor’s, 23.3% (7/30) master’s, and 13.3% (4/30) PhD-level education. Although younger adults showed a tendency toward higher educational attainment, this difference was not statistically significant (χ²3=4.09; P=.25). An a priori power analysis (2-tailed α=.05, 80% power) indicated that the sample size of 30 adults per group provides sufficient power to detect Cohen d∼0.74 for between-group comparisons and interaction effects (approximated as between-group differences in within-subject contrasts). Within-group paired contrasts have 80% power to detect dz∼0.53 (n=30) and dz∼0.37 when considering the full sample (N=60). These estimates represent conservative approximations relative to the mixed-effects modeling approach used in the primary analyses. All participants underwent screening to confirm good general health, as only healthy individuals were included in the study. Specifically, participants had no prior diagnosis of neurological or psychiatric disorders and reported no previous direct experience interacting with socially assistive robots. This sampling strategy was adopted to isolate age-related differences in CL responses under controlled experimental conditions while minimizing additional variability associated with clinical impairment. Accordingly, the study was conceived as an initial step toward understanding how assistance modality influences CL, with the intention of extending this line of research to more vulnerable populations in future work. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Nottingham Trent University Ethics Board (reference number 729) in March 2024. All participants provided written informed consent prior to participation and were informed of their right to withdraw at any time without consequence. The cognitive task used in this study was adapted from the B form of the Trail Making Test (TMT) [23]. The TMT was selected over alternative cognitive measures due to its engagement of multiple cognitive domains, including attention, visual search, executive functioning, and working memory. Consequently, the task required participants to simultaneously deploy multiple cognitive skills, closely reflecting the complexity of everyday activities. Specifically, the task used a 29.7×42 cm sheet of paper containing 52 randomly arranged circles (Figure 1). Half of the circles included English letters (A-Z), while the remaining circles contained numbers (1-26). A total of 7 task versions were created to minimize learning effects across repeated conditions. These versions were not intended to represent validated parallel forms of the TMT in a clinical sense; rather, they were task variants inspired by the TMT and designed to evoke comparable executive, attention, and visual search demands. For each version, the 52 circles were randomly distributed across the sheet to create distinct spatial arrangements while preserving the same basic task structure. Task versions were assigned randomly to participants across conditions, with each participant completing a different version in each condition. Objective performance was behaviorally evaluated based on the number of correctly connected pairs and the time required to complete the task (seconds). The supportive agent used in this study was the NAO 6 humanoid robot (United Robotics Group). This platform was selected for its commercial availability and relevance to real-world assistive deployments aligned with the study’s requirements, including speech production, speech recognition, and social interaction. Previous studies have shown that NAO (Aldebaran, Paris, France) functions as an SAR that is generally well-received by older and vulnerable populations [16]. The robot is 58 cm tall and can interact through speech, gestures, and facial tracking. Programming was implemented using Choregraphe software (version 2.8.7; Aldebaran) together with Python (Python Software Foundation) to enable interactive collaboration with participants in 3 of the 7 experimental conditions. The robot’s behavior was structured using block-based visual programming within a node flow interface. Interactions began with NAO tracking the participant’s face to simulate eye contact, followed by a self-introduction and delivery of initial task instructions. Speech recognition allowed the robot to repeat instructions upon participant request during an initial familiarization phase, which served as a vocal adaptation period responsive to participant feedback. After this phase, NAO did not repeat instructions to avoid disrupting task performance, although speech recognition remained active to manage pacing during the interaction. Indeed, the robot-participant interaction followed a structured and standardized dialogue logic designed to ensure consistency across participants while maintaining naturalistic turn-taking dynamics. Specifically, interaction sequences were organized into alternating robot-led instruction phases and participant execution phases. The robot presented task instructions verbally according to predefined scripts and advanced through task steps only after receiving explicit verbal confirmation from the participant (eg, “Okay” or “Done”), thereby regulating the interaction rhythm. The presentation of task instructions varied systematically according to CL conditions. In the low-load condition, the robot delivered one pair of items at a time, whereas in the medium- and high-load conditions, multiple pairs were provided sequentially within a single instruction block. The interaction pacing was therefore partially scripted, with fixed instruction sequences, but temporally modulated by participant responses, allowing limited responsiveness to individual execution speed. To ensure procedural reproducibility, the interaction logic combined fixed scripted content (instruction wording, sequence structure, and load manipulation) with controlled interaction triggers based on participant verbal feedback. A schematic representation of the interaction workflow is provided in Figure 2. The flowchart illustrates the decision nodes governing instruction delivery, response confirmation, and transition between interaction phases, providing a visual representation of the standardized dialogue protocol implemented across conditions. Moreover, to enhance transparency and reproducibility of the interaction protocol, a structured overview of the robot dialogue architecture, including interaction phases, scripted instruction templates, and response-triggered transitions, is provided in Table 1. Perceived cognitive load (PCL) was assessed using the National Aeronautics and Space Administration Task Load Index (NASA-TLX) [24], a brief, self-administered psychometric tool evaluating 6 dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. Each dimension was rated on a 20-point scale, with higher scores indicating greater perceived strain. The NASA-TLX was chosen for its robust psychometric properties and demonstrated superiority over alternative subjective workload measures [25], making it well-suited for assessing PCL in this study. In line with the primary aim of capturing overall perceived workload during assisted task performance, the total NASA-TLX score was used as a global indicator of subjective CL. Although the instrument allows examination of individual workload dimensions, the aggregated score has been widely used to provide a comprehensive estimate of perceived task demand, particularly in applied human-technology interaction research [25]. To objectively assess physiological stress responses associated with the CL tasks, saliva samples were collected immediately before and after each condition (ie, robot-assisted and human-assisted). Samples were obtained using a synthetic swab (Salivette Cortisol, Sarstedt), instructing participants to slowly move the swab around the oral cavity for 2 minutes, in accordance with the manufacturer’s instructions. All samples were maintained on crushed ice throughout testing, which did not exceed 45 minutes. Subsequently, saliva samples were centrifuged at 1000×g for 2 minutes (Espresso Microcentrifuge, Thermo Scientific), and the supernatant was transferred into 1.5 mL Eppendorf tubes and stored at −80 °C until analysis. Quantitative determination of salivary cortisol concentrations was performed using commercially available enzyme-linked immunosorbent assay kits (Salimetrics), following the manufacturer’s recommended procedures. Salivary cortisol was included as a physiological indicator of stress-related arousal associated with cognitive effort. While cortisol does not represent a direct measure of CL, it provides complementary information regarding the neuroendocrine response to task-related strain, allowing integration with behavioral and subjective workload indicators. Moreover, salivary cortisol was sampled only before and after each assisted block, and not after the independent baseline condition, because the physiological outcome was intended to capture the cumulative response associated with the two assistance modalities rather than transient changes across individual CL levels or during unassisted performance. This choice was based on both theoretical and methodological considerations, including the temporal dynamics of the hypothalamic-pituitary-adrenal axis, as salivary cortisol typically peaks approximately 20‐40 minutes after stress onset. Accordingly, cortisol was treated as a block-level marker of stress-related arousal, and restricting sampling to the assisted conditions helped reduce participant burden while preserving the feasibility and ecological validity of the experimental protocol. Each participant completed the experimental protocol individually across seven conditions: one baseline condition performed independently, three robot-assisted conditions at varying CL levels (low, medium, and high), and three human-assisted conditions corresponding to the same CL levels. The baseline condition was systematically administered at the beginning of the session to provide a common, unassisted reference point for all participants. Within each assisted block, CL levels were presented in a fixed progressive sequence (low, medium, and high) in both the robot-assisted and human-assisted conditions. This choice was made to ensure a gradual and controlled increase in task demands, thereby minimizing potential confusion associated with abrupt changes in difficulty and maintaining a stable and predictable interaction structure, particularly for older adults. In contrast, the order of the assisted blocks (robot-assisted vs human-assisted) was fully counterbalanced across participants to control for order effects at the level of assistance modality. Both assisted conditions were administered within the same experimental session. Consequently, the precondition saliva sample for the second assisted block was collected immediately before its onset, following completion of the preceding condition and a brief transition phase required to reset the materials and prepare the subsequent task. While counterbalancing mitigates systematic sequence effects at the level of condition order, the absence of an extended washout interval implies that a residual carryover influence from the first assisted condition on the precondition cortisol value of the second block cannot be entirely excluded. The total duration of the experimental session was approximately 30 minutes. After providing informed consent, the participant was seated at a desk and presented with the first task sheet. In the baseline condition, the participant was instructed to connect alternating numbers and letters in reverse order (ie, starting from the highest number and the last letter) as quickly and accurately as possible using a marker. This task was designed to elicit CL by engaging multiple cognitive domains, including semantic memory, working memory, visual search, and processing speed. Specifically, the participant was required to recall the reverse sequence of alphanumeric items, maintain this sequence in working memory, and identify and connect the corresponding circles on the sheet. During the three robot-assisted conditions, the NAO humanoid robot acted as an interactive support agent (Figure 3). At the beginning of each assisted condition, the robot greeted the participant, introduced the task, and repeated the instructions upon request. Throughout task execution, the robot verbally provided the pairs of circles to be connected, thereby reducing reliance on semantic memory and allowing participants to focus on visual search and motor coordination. The robot advanced through the sequence based on participant responses, waiting for a verbal confirmation (eg, “Okay”) before announcing the next pair. To prevent learning effects, the sequence of pairs was randomized across trials. CL was systematically manipulated as follows: in the low-load condition (second and fifth condition), the robot presented one pair at a time; in the medium-load condition (third and sixth condition), two to four pairs were provided simultaneously; and in the high-load condition (fourth and seventh condition), five to seven pairs were delivered concurrently. The robot did not repeat any pair throughout the session. The human-assisted condition was administered by a trained experimenter who followed a standardized script designed to mirror the robot-assisted protocol in content, sequence, and CL manipulation. The experimenter delivered the instructions verbally using the same wording as the robot prompts, maintained the same load-specific structure, and waited for the same participant verbal confirmations before proceeding to the next step. A brief initial familiarization phase was also included to ensure procedural parity with the robot-assisted condition: during this phase, the experimenter presented the task instructions and allowed participants to request clarification, analogous to the vocal adaptation period implemented in the HRI condition. Following familiarization, the interaction proceeded according to the predefined script without additional instruction repetition. The only difference between conditions was the modality of delivery, with all other procedural elements held constant. A structured overview of the human-assisted dialogue, emphasizing the fidelity rules for the adherence to the HRI condition, is provided in Table 2. At the end of each condition, including baseline, robot-assisted, and human-assisted tasks, participants completed the NASA-TLX questionnaire to evaluate PCL. As described previously, saliva samples were collected at the beginning and the end of both the robot-assisted and human-assisted conditions to capture physiological markers of CL, yielding four samples per participant. For data analysis, two independent experimenters (SV and RV) assessed task performance across all conditions. Performance measures included accuracy (number of correctly connected pairs) and completion time (in seconds). Subjective workload was quantified using NASA-TLX scores, while physiological responses were evaluated by comparing pre- and postcondition cortisol concentrations across the robot- and human-assisted settings. Linear mixed-effects models were applied using restricted maximum likelihood estimation to examine differences across the primary outcome variables, including NASA-TLX scores, task performance indices, and cortisol responses. Participants were included as random intercepts to account for interindividual variability in baseline performance and physiological measures. A type III ANOVA with Satterthwaite approximation was used to assess the main effects and interactions of condition (robot-assisted vs human-assisted), CL level (baseline, low, medium, and high), and age group (younger vs older adults). Condition at varying load levels was modeled as a within-subject factor, while age group was treated as a between-subject factor. Post hoc comparisons were performed on estimated marginal means and mean differences (Mdif) to further explore significant main effects and interactions. Tukey-adjusted estimated marginal means comparisons were used to correct for multiple testing. In line with the study’s objectives, particular attention was devoted to the interaction between age group and experimental condition to clarify differential patterns of CL management and task performance across age cohorts. Mean (SD) values for accuracy (number of correctly matched pairs), completion time (in seconds), and PCL across the seven experimental conditions, as well as cortisol levels (nmol/L) measured before and after the robot-assisted and human-assisted conditions, are presented in Tables 3 and 4. aPCL: perceived cognitive load. bCL: cognitive load. aCortisol levels (nmol/L) were measured at the beginning (pre) and end (post) of the human-human interaction and human-robot interaction conditions. Regarding performance, analysis of accuracy revealed significant interactions between age group and condition (F1, 404.53=6.50; P=.01) as well as between age group and CL level (F3, 403.45=5.88; P<.001). Post hoc comparisons indicated that older adults exhibited lower accuracy in the HRI condition compared with younger adults (Mdif=1.97, SE=0.58; t106=3.34; P=.001; Figure 4A) and also relative to the human-assisted condition (Mdif=1.54, SE=0.43; t403=3.57; P<.001; Figure 4B). Additionally, older adults demonstrated significantly lower accuracy than younger adults at medium (Mdif=2.75, SE=0.73; t219=3.75; P<.001) and high CL levels (Mdif=1.64, SE=0.73; t219=2.23; P=.03), whereas no significant differences were observed at low load (Mdif=1.16, SE=0.73; t219=1.58; P=.12) or in the baseline condition (Mdif=−0.80, SE=0.73; t217=−1.09; P=.27; Figure 4C). A significant interaction between condition and CL level was also observed (F1, 403.45=5.83; P<.001). Across all participants, post hoc tests revealed higher accuracy in the human-assisted compared with the robot-assisted condition at low (Mdif=1.25, SE=0.61; t404=2.05; P=.04) and medium CL levels (Mdif=2.58, SE=0.61; t404=4.21; P<.001), whereas no significant differences emerged at high CL (Mdif=−0.79, SE=0.61; t404=−1.29; P=.19; Figure 4D). Analysis of task completion time revealed a significant interaction between age group and CL level (F3, 402.85=23.73; P<.001). Post hoc comparisons indicated that younger adults completed the task faster than older adults at baseline (Mdif=−123.73, SE=18.70; t114=−6.62; P<.001), as well as under low (Mdif=−115.95, SE=18.70; t115=−6.19; P<.001) and medium CL conditions (Mdif=−75.16, SE=18.70; t115=−4.01; P<.001). No significant difference was observed at high CL (Mdif=1.73, SE=18.70; t115=0.09; P=.92; Figure 5). Analysis of NASA-TLX scores revealed significant interactions between age group and condition (F1, 403.45=4.58; P=.03) as well as between age group and CL level (F3, 403.12=8.53; P<.001). Regarding the interaction between age group and condition, post hoc comparisons showed that older adults reported significantly higher scores during the robot-assisted condition (mean 57.60, SE 2.13) compared with the human-assisted condition (mean 54.3, SE 2.13), with an estimated Mdif of −3.29 (SE=1.16; t403=−2.83; P=.005). No significant difference was observed in younger adults (Mdif=0.25, SE=1.18; t404=0.21; P=.83), indicating that older adults experienced greater perceived CL when interacting with the robot compared with human support (Figure 6A). For the interaction between age group and CL level, post hoc analysis revealed that older adults reported significantly higher total scores at the low CL level (mean 43.8, SE 2.28) compared with younger adults (mean 37.2, SE 2.29; Mdif=−6.63, SE=3.23; t89.0=−2.05; P=.04), suggesting that older adults perceive more CL than younger adults under low-load conditions (Figure 6B). Analysis of the physiological measure of salivary cortisol concentration revealed a significant interaction between age group, condition, and time (pre vs post; F1, 156=5.16; P=.02). When analyzed separately by age group, older adults demonstrated a significant interaction between condition and time (F1, 156=10.32; P=.001), whereas younger adults showed no significant effect (F1, 156=0.01; P=.90). Post hoc comparisons indicated that older adults exhibited a significant increase in cortisol concentrations from baseline to postintervention during the HRI condition (Mdif=−5.65, SE=1.31; t156=−4.31; P=.001; Figure 7). This study aimed to investigate how HHI and HRI support the management of CL during task performance in younger and older adults, integrating behavioral, subjective, and physiological measures. By including a baseline condition without assistance and systematically varying CL levels, this research extends the literature on cognitive ergonomics in aging and technology-assisted contexts by providing an applied evaluation of assistive support modalities that are increasingly proposed as digital health solutions for aging populations. Importantly, these findings should also be interpreted within a translational digital health perspective. While assistive technologies are frequently evaluated in controlled experimental settings, their successful implementation depends on sustained usability, user acceptance, and ecological integration into daily routines. In this context, increases in CL or physiological load, even when accompanied by performance improvements, may represent critical barriers to long-term engagement and adherence. Consequently, evaluating assistive systems through a multidimensional framework that captures both performance outcomes and user burden is essential to inform scalable and sustainable deployment strategies. Consistent with previous evidence indicating age-related declines in processing speed and working memory capacity [26,27], older adults demonstrated lower accuracy and required more time to complete tasks as CL increased. These differences were most pronounced at medium- and high-load levels, supporting theoretical frameworks positing that increased task complexity disproportionately affects older adults due to age-related cognitive changes [28]. Notably, at low CL levels, accuracy differences between age groups were reduced, suggesting that minimal task demands may better align with preserved cognitive capacities in older adults, enabling performance comparable to that of younger adults. Importantly, these findings reinforce the need to evaluate assistive technologies for older adults not only under simplified or optimal conditions, but also across varying levels of cognitive demand that more closely reflect real-world task complexity. The interaction between type of assistance and CL revealed that HHI generally supported higher accuracy compared with HRI, particularly at low- and medium-load levels. This finding aligns with research suggesting that human support can more flexibly and adaptively accommodate the nuanced needs of older adults, thereby reducing extraneous CL [29]. The absence of significant accuracy differences between HRI and HHI at high CL may tentatively reflect a ceiling effect, whereby performance approached an upper limit, reducing sensitivity to differences between assistance modalities. However, this interpretation should be treated with caution, as performance saturation was not formally tested in this study. Subjective measures of PCL, assessed via NASA-TLX scores, indicated that older adults consistently reported higher CL during HRI compared with HHI, particularly under low-load conditions. This finding is especially relevant from a usability and adoption perspective, as increased perceived workload under otherwise manageable task demands may negatively influence trust, acceptance, and sustained engagement with assistive technologies. This result suggests that even minimal interactions with robotic agents can impose additional extraneous cognitive demands for older adults, potentially due to challenges in speech recognition, turn-taking, or the social-cognitive demands of interpreting robotic behavior [30]. It is important to acknowledge that certain technical constraints are inherent to current HRI systems. Features such as response latency, variability in speech recognition, and predefined interaction pacing represent realistic operational characteristics of SARs rather than limitations of the experimental design. These factors may require users to allocate additional attentional and executive resources to manage turn-taking and system feedback, thereby contributing to increased extraneous CL. Such demands may be particularly relevant for older adults, who may be more sensitive to temporal unpredictability and reduced interactional flexibility. Recognizing these constraints as ecological features of present technologies highlights key targets for future system refinement, including more robust speech processing and adaptive interaction pacing, which may ultimately improve usability and reduce cognitive burden. By contrast, younger adults showed no differences in PCL between assistance types, suggesting that HRI may be more readily accommodated by individuals with higher baseline cognitive flexibility and greater familiarity with technology [31]. Physiological measures of salivary cortisol concentrations corroborated these findings, revealing increased cognitive strain during HRI for older adults, with significant postintervention elevations observed in the HRI condition. This is consistent with prior literature demonstrating heightened physiological reactivity in older adults to tasks perceived as challenging or unfamiliar [32]. The convergence of behavioral, subjective, and physiological indicators strengthens the interpretation that HRI, in its current form, may elicit latent stress responses in older users that are not fully captured by performance metrics alone, underscoring the value of multimodal assessment frameworks in digital health evaluation. Indeed, the use of the total NASA-TLX score allowed the capture of participants’ global perception of workload across interaction modalities, providing an ecologically valid estimate of overall task burden. Complementarily, cortisol responses were interpreted as markers of physiological stress associated with cognitive effort rather than direct indicators of CL per se. The consistency of these measures supports a multidimensional interpretation of user strain during assistive interaction. The absence of similar effects in younger adults underscores the differential impact of HRI on cognitive stress across age groups and highlights the importance of integrating physiological, subjective, and performance-based measures to comprehensively assess CL. Together, these findings highlight the complex interplay among aging, CL management, and the type of assistance provided during task performance. Previous research on behavioral and cognitive-affective regulation has similarly emphasized the need for individualized and context-sensitive approaches in psychological assessment and intervention [33,34]. Within the context of assistive digital technologies, these results suggest that “one-size-fits-all” interaction models are unlikely to meet the needs of older adults with heterogeneous cognitive profiles. While robotic assistance offers scalable and consistent support, its current implementation may inadvertently increase extraneous CL for older adults, particularly in tasks requiring social-cognitive engagement. In contrast, human assistance appears to mitigate these challenges, supporting better performance and lower perceived strain under comparable task demands. From a digital health design and deployment perspective, these findings suggest that SAR systems should prioritize the minimization of extraneous CL through simplified interaction structures, improved speech recognition robustness, and adaptive pacing strategies that respond to individual user performance and stress indicators. Incorporating real-time or near–real-time indicators of CL could enable dynamic adjustment of assistance, reducing unnecessary cognitive strain and improving user experience. Moreover, hybrid models that combine robotic assistance with intermittent human support may represent a pragmatic transitional approach, particularly in early stages of adoption or in cognitively demanding tasks. These considerations are particularly relevant for large-scale implementation strategies, where technologies must remain cognitively sustainable across prolonged and repeated use in real-world environments. Beyond implications for system design and deployment, this study also offers methodological insights that strengthen the interpretation of technology-assisted CL effects. Importantly, the inclusion of an independent baseline condition in this study strengthens the interpretation of the observed effects by enabling direct comparison between assisted and unassisted task performance. This design feature allows differentiation between genuine support-related benefits and task facilitation effects that may arise simply from external guidance, irrespective of the modality of assistance. By contextualizing both human and robotic assistance against independent performance, the findings demonstrate that while assistance generally improves task outcomes, the cognitive and experiential costs associated with different support modalities vary substantially, particularly for older adults. An additional methodological strength is that the human-assisted condition was operationalized through a standardized script matched to the robot-assisted protocol in wording, sequencing, and load manipulation. This design choice reduced the risk that differences between conditions were attributable to variability in human delivery style rather than to the assistance modality itself. This comparative framework enhances internal validity and provides a more robust basis for evaluating assistive technologies as potential substitutes or complements to human support in real-world digital health contexts. The observed interaction patterns suggest that technological interventions designed for older adults should prioritize reducing extraneous CL by simplifying robot interfaces, improving speech recognition accuracy, and implementing adaptive pacing tailored to individual users. Indeed, although older adults view social robots as a potential tool to support their daily activities, our results further highlight the importance of designing such systems according to users’ specific needs [5]. Additionally, multimodal support strategies, including visual cues and simplified verbal prompts, may enhance the usability and effectiveness of HRI by aligning it more closely with the cognitive profiles of older adults. This study suffers from some limitations. For instance, the generalizability of these findings. Although the inclusion of healthy younger and older adults allowed us to examine age-related differences under controlled conditions, the results cannot be directly generalized to more vulnerable groups, such as individuals with mild cognitive impairment or other clinical conditions for whom SARs may be particularly relevant. Future studies should therefore replicate this design in clinical and functionally vulnerable populations to determine whether the observed patterns of behavioral, subjective, and physiological response are preserved, attenuated, or amplified in contexts of greater cognitive vulnerability. Then, the seven task versions were not formally tested for equivalence in difficulty. Accordingly, they should not be interpreted as psychometrically validated parallel forms of the TMT. Although random assignment of versions across participants and conditions was used to reduce systematic version effects and support internal validity, residual differences in difficulty between task versions cannot be entirely excluded. Future studies should include formal equivalence testing of task variants or adopt a fully validated parallel-form procedure if the task is to be used repeatedly across conditions. A further methodological consideration concerns the fixed progression of CL levels within each assisted block. Although this approach ensured a controlled and gradual increase in task demands and supported procedural consistency across participants, it does not allow complete separation of CL effects from potential sequence effects. Future studies may benefit from counterbalancing or randomizing load sequences, although such designs should carefully consider the potential impact of increased task-switching demands, particularly in older populations. An additional limitation concerns the interpretation of the nonsignificant differences in accuracy between conditions at high CL. Although a ceiling effect may provide a plausible explanation, this possibility was not directly tested through specific analyses of performance saturation. As such, this interpretation should be considered tentative, and future studies should include measures or analytical approaches specifically designed to detect ceiling effects. Moreover, the sequential structure of the assisted blocks should be considered when interpreting the cortisol findings. The order of robot-assisted and human-assisted conditions was fully counterbalanced, which reduced the likelihood that the results were driven by presentation order. However, because the precondition cortisol sample for the second block was collected immediately after completion of the first assisted condition, a residual carryover effect cannot be completely excluded. This limitation is particularly relevant for salivary cortisol, given its temporal dynamics and slower recovery profile. Future studies may benefit from longer recovery intervals or separate-session administration to further reduce potential carryover effects while preserving the advantages of counterbalanced designs. Another important methodological consideration is that salivary cortisol was assessed only in the assisted conditions and at the block level, whereas behavioral and subjective outcomes were collected across all load levels, including baseline. Consequently, the physiological findings should be interpreted as reflecting differences between robot-assisted and human-assisted interaction, rather than as load-specific effects or as changes relative to the independent baseline condition. This design choice was consistent with the intended use of cortisol as a cumulative marker of stress-related arousal; however, it also limits direct comparison between physiological and other outcome domains and should be considered when interpreting the multimodal findings. Future research should examine longitudinal exposure to robotic systems to determine whether increased familiarity reduces cognitive and physiological strain in older adults, potentially enhancing acceptance and efficacy. Such work is essential to distinguish short-term novelty or learning effects from stable interaction patterns that are likely to emerge during real-world deployment. Expanding physiological monitoring to include measures such as heart rate variability and electroencephalography could further elucidate the real-time cognitive dynamics associated with HRI and HHI across age groups. In conclusion, this study provides important evidence regarding CL management in aging populations within assisted task contexts, emphasizing the need to tailor support strategies to individual cognitive capacities. While HRI holds promise for facilitating aging-in-place initiatives, its effectiveness depends on careful interaction design and adaptive support mechanisms that ensure robotic assistance reduces, rather than amplifies, cognitive and physiological burden. Optimizing these systems is critical to supporting autonomy, usability, and quality of life in the context of active aging. Although both HHI and HRI improved performance compared with independent task completion, the effectiveness of assistance was strongly dependent on the type and complexity of support, particularly in older adults. Human assistance consistently supported higher accuracy and lower perceived workload, whereas robotic assistance, despite its promise for scalable and consistent support, was associated with increased perceived and physiological CL in older adults, especially under low and medium task demands. Overall, these findings highlight the importance of designing adaptive, age-sensitive digital assistive systems that minimize cognitive burden through simplified interaction, responsive pacing, and multimodal support. Importantly, even when assistive technologies improve task performance, increases in CL or physiological load may limit usability, scalability, and sustainable real-world deployment in aging populations. By demonstrating the value of multimodal CL assessment and tailored support strategies, this study provides actionable evidence to guide the design, evaluation, and deployment of assistive technologies that genuinely promote autonomy and quality of life in the context of active aging. Acknowledgments The authors would like to acknowledge all the participants who voluntarily took part in the study. This work was supported by the Engineering and Physical Sciences Research Council and National Institute for Health and Care Research funds (grant number EP/W031809/1, IMACTIVE). The data presented in this study are available at [35]. Conceptualization: DM, ADN, JH, and SCMethodology: SV and DMSoftware: SVFormal analysis: RVData curation: SVWriting - original draft preparation: SV, RV, and NCWriting - review and editing: JH, SC, ADN, and DMSupervision: DM and SCFunding acquisition: DM and ADN Conceptualization: DM, ADN, JH, and SC Methodology: SV and DM Software: SV Formal analysis: RV Data curation: SV Writing - original draft preparation: SV, RV, and NC Writing - review and editing: JH, SC, ADN, and DM Supervision: DM and SC Funding acquisition: DM and ADN None declared. None declared. Edited by Matthew Balcarras; submitted 06.Mar.2026; peer-reviewed by Gabriele Pesimena, Simona Massimino; final revised version received 09.Apr.2026; accepted 09.Apr.2026; published 01.Jun.2026. © Simone Varrasi, Roberto Vagnetti, Nicola Camp, John Hough, Alessandro Di Nuovo, Sabrina Castellano, Daniele Magistro. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 1.Jun.2026. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. Please cite as: Varrasi S, Vagnetti R, Camp N, Hough J, Di Nuovo A, Castellano S, Magistro D Human and Robot Assistance for Cognitive Load in Younger and Older Adults: Multimodal Within-Subject Experimental Study J Med Internet Res 2026;28:e94738 doi: 10.2196/94738 PMID: 42224389 Journal of Medical Internet Research ISSN 1438-8871 Copyright © 2026 JMIR Publications

The 'Neo' robot deliberately falls short of realism. (Handout) The 'Neo' robot deliberately falls short of realism. (Handout) At Cooper’s Brewery in Adelaide, the largest family-owned brewing business in Australia, forklifts glide around the sizable factory floor. With ease, they shelve boxes of lagers and ales. But nobody is at the wheel. In fact, there isn’t a wheel at all. Or a seat. These forklifts are automated guided vehicles (AGVs), a pretty old form of robotics that uses lasers, floor markers or other navigation systems to follow pre-planned paths. People had always imagined that robots would look like us, but these forklifts hint at a different future — one in which robots are made in the image of the work, not the worker. For the question of whether robots will be the same shape as humans is not a settled one. In fact, it is a point of some debate in the robotics sector. Elon Musk promises legions of humanoid Tesla robots, poised to take over manual labor and build a colony on Mars. And his is not a particularly wacky view; other executives, including those less given to overconfident predictions, have made similar forecasts. In his lecture delivered to the Cosmos Institute last month in Oxford, the Anthropic co-founder Jack Clark predicted that, by April 2028, bipedal robots will “begin to do useful work in the real world in partnership with human tradespeople”. While being bipedal does not mean a robot is identical to a human, it certainly denotes it being closer in form to us than to a forklift. As recently as this time last year, a wide rollout of electronic colleagues looked unlikely. Even in China, the world leader of the humanoid robotics market, the stumbling and malfunctioning first cohort of robot competitors in the Beijing half-marathon were treated as more of an entertaining sideshow than a serious sign of things to come. Clark himself appeared relatively pessimistic about the capabilities of humanoid robots at the time; in response to an Amazon research paper about the performance of a specialized robotics system for stowing items in the company’s warehouses, he noted that state-of-the art automation was coming from designing highly specific hardware and “carefully structuring your system around a few core tasks”. The experiment, he wrote, “should temper our expectations for bipedal robots leading to some massive improvement in automation — at least in the short term.” What changed? It may simply be that this massive improvement has arrived. At this year’s Beijing half-marathon, several robots outpaced human participants, even breaking records. This feat had serious implications, demonstrating the kind of durability that might soon make the technology suitable for industrial applications. On that basis, Clark’s prediction — that more human-like machines will start trickling into the workforce next year — starts to sound plausible, though we can expect humans would still be on-site to correct for their defects. Manual labor will be slowly automated. Or it may be that Clark believes the underlying AI systems are becoming so powerful, so fast, that the obstacles will be swept away. The robots’ cognitive abilities will catch up with their strength and dexterity. Manual labor will be rapidly automated. “Truly general intelligence would obviate some of the flaws,” Clark wrote in his sceptical 2025 missive. “So if bipeds arrive at the same time as a generally capable intelligence, I’ll need to eat my words.” This is now the precipice at which we find ourselves. As AI improves, those in the field say robots will become more capable of learning for themselves, of adapting to new environments without pre-programming, and even employing reasoning. They’ll be easily able to work in an Amazon warehouse — and then some. The ultimate prize, as Clark says, would be truly general intelligence: a system that can turn its hand to any task without specific training. Many imagine that, in this scenario, the machine most suited to carrying around a general, human-like intelligence would be in the shape of a person. After all, the world is built for humans, and humans have the dexterity to adapt to a vast range of manual tasks. Many industrial robots, by contrast, are made to complete one specific task from a set point. Or else they operate entirely inside facilities built for them: look, for example, at the fulfillment centers of British grocery delivery giant Ocado, where a “hive” of robots roll across a grid system, picking and packing orders. The question is whether the economy will demand robots that can venture into unexpected environments. For this, humans are far more adaptable, taking on stairs and uneven terrain. We climb ladders and traverse scaffolding and even rooftops. It’s hard to imagine a machine able to do all that if it isn’t shaped like us. Clark is probably correct that the bipedal assistants will arrive sooner rather than later, precisely because they might be a stopgap between human work and a totally different way of making things. The trades of the future could look more like the work of British startup Automated Architecture, which makes mobile, robotic micro-factories that can build pre-fabricated components of a house quickly and closer to its final site. Inside the micro-factory, one large robotic arm performs tasks that might typically require several different production lines, producing floors, walls and roofs. Human workforces are still needed to assemble the timber-frame panels, but the process minimizes how much construction is needed on-site by pre-making all the elements for the shell of a building. Think of it like buying a gingerbread house kit instead of baking all the pieces from scratch. In this way, the robotics of industry can go beyond the human approach to a task. Why climb a ladder when you could send a drone? These are already used for surveys and roof inspections. With the right dexterity, perhaps they will be able to perform the fixes as well. Humanoids, argues Josef Chen, the founder of London-based restaurant industry robotics firm Kaikaku AI, are like the equivalent of a pick-up truck on a construction site. “Even though it’s not used for any serious work, people just have it for optionality.” In this analogy, you turn to a digger for digging, a crane for lifting. You wouldn’t expect the truck to do those specialist tasks, even though it can be useful for transportation, towing, or navigating tricky ground. Those who believe in the utility of the human-shaped robot, though, believe that getting it right could unlock huge gains — and huge threats to the human workforce. As Clark wrote back in 2023, “true economic growth from AI happens when you don’t need to design for robots”. If an army of person-shaped droids start arriving in human workplaces, with no adjustments needed for them, it is easy to envision how this would accelerate workforce displacement. Matvey Boguslavskiy, a hardware researcher and director of the Society of Technological Advancement, doesn’t buy that. “I think the future economy is going to have robots with many different form factors, doing many different things,” he says. Indeed, if robot arms or vehicles or dog-like quadrupeds prove vastly more efficient than humans, the argument about whether the world has to be redesigned for robots falls flat: the economic incentive will be enough to justify a redesign. By Alys Key So if applications in the workplace favor the specialized machinery end of the robotics scale, then perhaps the promise of the humanoid robot is in the home. Whereas a factory might have space for several machines, each built for a different purpose, homes are more cramped. It would be difficult and expensive to accommodate an ironing robot, a cooking robot, a gardener robot, and so on — but what if they were all rolled into one? . But here the robots will encounter another obstacle: human disquiet. There is something freakish about Tesla’s Optimus, or Boston Dynamics’ Atlas. The way they are lined up like an army in some promotional videos doesn’t help. It’s also in how close they are to being human-shaped, yet how far, almost like there’s a real person under there wearing a creepy costume. It’s the almost-ness that gets you. The heads are almost the right size, the hands almost lifelike — if often clothed in serial killer black gloves. It may be that we’re too conditioned by science fiction horrors to accept humanoid robots. But the reason those fictional visions terrify us in the first place is because they tap into a primal fear — of a monster, or döppelganger. To make robotics more palatable in the home, many companies are making devices less threatening by ramping up their apparent cuteness. Pixar has a lot to answer for here. Everyone I speak to on this subject cites Wall-E and the success the animation studio had in making a boxy, metal robot into a lovable character. One startup robotics lab even brought in Toy Story screenwriter Alec Sokolow as creative director when developing Ongo, a smart desk lamp with big, Disney-worthy eyes. There seems to be strong consumer demand for such devices, at least among gadget obsessives. There are countless Kickstarters for small robotic companions and widgets that receive hundreds of thousands of dollars in advance orders. However, an interactive paperweight can only do so much. To load the dishwasher, you need grip, spatial reasoning, and a way to get high enough to open or close it. If we really want a smoother life with no chores, there would seem to be few options beyond either hiring another human to help or letting humanoids into the home. What’s the way forward? Boguslaviskiy thinks one of the most useful forms will be the small, wheeled vehicle. We have already seen these catch on for vacuuming and mowing lawns autonomously. Why not for other tasks too? Or if the human shape proves useful, the most acceptable forms might be some compromise between the humanoid and the cute. Take a look at Fauna Robotics, a New York-based company which was acquired by Amazon in March. Their products are deliberately fun to look at, with bright colors and jaunty eyebrows. Crucially, they avoid uncanny valley reactions by not trying to make the robot — named Sprout — look too human. It has Lego-like hands and a head that resembles a Wi-Fi modem. It looks like what it is: a machine. Sprout is also small, at 107cm tall, so won’t tower menacingly over a child. Or there’s Memo, from California-based Sunday Robotics. Sometimes described as humanoid, the robot nevertheless does away with cumbersome feet, instead rolling around on wheels. With its permanent baseball cap, it looks more like a character in a children’s film than a chilling centurion of the future. These non-threatening interfaces serve a deeper purpose than just saving children a fright. They strive to make it possible for us to live alongside intelligent machines. Though AI continues to seep into life and work, there are still many people who avoid using the technology or even encountering it as much as possible. When AI enters the physical world, it becomes less easy to ignore, and triggers in some people a sense of revulsion and anger. We have seen this in the repeated attacks on delivery robots and driverless cars. People in the tech industry who delight in the miracle of creating motion, who have fun hacking together hardware projects on the side, perhaps underestimate how far there is to go on public acceptance of robots. Creepy, faceless, six-foot Cybermen are probably not going to help. Alys Key is a freelance journalist who covers technology, business and policy. She writes the UK 2.0 newsletter on Substack. Δ Δ We welcome applications to contribute to UnHerd – please fill out the form below including examples of your previously published work. Please click here to submit your pitch. Please click here to view our media pack for more information on advertising and partnership opportunities with UnHerd.

BOOM Library has released MECHS & ROBOTS, which they describe as a professional sound effects library built to solve a problem every mech sound designer knows: starting from scratch on every project because nothing in your library was built to work together at scale. Here's the details direct from the company... Eight fully designed robot characters across four size classes share a single design language, so a Microbot and a Titan can coexist in the same session and occupy the same sonic space cleanly. The Construction Kit provides the source material to extend any character or build new ones, giving sound designers a complete mechanical design system in a single library. MECHS & ROBOTS is available in three editions: Designed, Construction Kit, and Bundle. About The Library The Designed edition contains eight fully realised robot characters: Microbot and Nanotron (Small), Android, Automaton, and Summoner (Medium), Gunbuster, Colossus, and Titan (Large), plus Extra Large characters. Each includes 12 sound categories and up to 57 individual variations. Sound categories include Footstep, Movement, Bodyfall, Damage Impact, Transform, Dismantle, Calculate, Expression, Rotation, Power Up, Power Down, and Idle sequence. Each character includes Footstep (10 variations) and Movement (10 variations) files. The Construction Kit is organised into four component categories: Metal, Mechanical, Servo, and Synthetic. Metal recordings cover footsteps, movements, impacts, scrapes, rattles, and debris at small, medium, and large sizes. Mechanical recordings include gears, clunks, clicks, levers, slides, and rotations sourced from industrial and vintage mechanical objects. The Servo category spans six motor configurations: plastic and metal housing in small, medium, and large, captured from real motors, including 3D printer arms, servo-driven industrial equipment, and robot components. The Synthetic category provides granular and wavetable-processed elements, including braams, glitches, textures, power-up sweeps, telemetry sequences, and vocal-quality droid sounds. All files are delivered at 96kHz / 24-bit WAV with full UCS-compliant metadata embedded for use with Soundminer, Basehead, and Soundly. Axel Rohrbach, Creative Director of BOOM Library, said: "Every mech project we've seen starts the same way: designers sourcing and patching together elements that were never built to work at different scales. MECHS & ROBOTS fixes that. One coherent system, from nanobots to ship-sized mechs, with the designed characters and the raw material to build your own." Key Specifications: Pricing and Availability: MECHS & ROBOTS is available now. Regular Pricing – Designed: $139/119€ / Construction Kit: $209/179€ / Bundle: $265/229€ Introductory 20% Off for 2 weeks – Designed: $111.20/€95.20 / Construction Kit: $167.20/€143.20 / Bundle: $212.00/€183.20 Promo pricing ends June 11th, 2026 More information: Andy Mac shows us around Chat DSP M4L and more 2600 & 101 had a baby Port names, internal routing and more Works of Art for Your Eurorack Modules Deep into retro hardware Copyright Sonic State Ltd © 1995-2026. All rights reserved. Reproduction in whole or in part in any form or medium without express written permission from Sonic State is prohibited.

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Seoul : There were no naked robots in sight at a fashion show held in Seoul with a high-tech twist, where pairs of people and humanoids hit the catwalk in matching outfits. A tasselled blue Texan-style ensemble -- complete with a cowboy hat for the robot -- and a retro silver puffer jacket were among the looks showcased at the event on Thursday. Each human model and their shorter android companion took turns to strut their stuff in unison on stage. The designs, including silky dresses and billowing space-age black trousers like those worn by rock star David Bowie in the 1970s, were carefully fitted to the robots' skeletal frames. Galaxy Corporation, the entertainment company behind the display, said it was meant to ask: "How can humans and robots coexist?" "We realised that robots, too, need to wear clothes," CEO Choi Yong-ho said. "Just as every human being is unique, we believe that every single robot should also be distinct." The clothes were designed by the company, whose spokesperson said it hopes to launch them under the brand name "MACH 33" at the end of the year. The robot models at the Seoul fashion show appeared to be a humanoid made by Chinese startup Unitree, which are popular due to their relatively low cost. Increasingly dexterous robots have proven themselves capable of performing choreographed dances, participating in races, and even able to land backflips. Financial services firm Morgan Stanley predicts the world could have more than a billion humanoids by 2050. But fully automated robots -- using emerging physical AI technology -- are still rare, with most impressive displays remotely operated or pre-programmed.

NEW YORK, May 31. /TASS/. US-based Foundation Future Industries related to the family of President Donald Trump held trials of android robots in the combat operations zone in Ukraine, CNBC television said, citing the company CEO. According to CNBC, the company sent two Phantom MK-1 model robots for the pilot demonstration. This was the first case of using robots in the combat zone. Their core function was to deliver ammunition to the frontline, company CEO Sankaet Pathak said. The model has a number of weaknesses, the TV channel said. Its carrying capacity is not above 20 kg, and the body is not water-proof. The short time of robots’ independent operation became one more obstacle to their large-scale use. The company plans now to send an improved version to Ukraine. The Phantom 2 will boast "superman" abilities, and its carrying capacity will be twice above the one of the Phantom MK-1.

Personalise the news and stay in the know Emergency Backstory Newsletters 中文新闻 BERITA BAHASA INDONESIA TOK PISIN Find any issues using dark mode? Please let us know By Meagan Dillon Topic:Biotechnology Industry Mon 13 Mar 2023 at 6:17am Scientists at a South Australian university are using biology from insects to build robots with a brain – technology that could become a game changer for police, defence and national security. "I'm giving a robot a brain so it can understand its environment," said Flinders University associate professor for autonomous systems, Dr Russell Brinkworth. His biologically-inspired robots have the ability to not just take a picture of the world, but interpret the surrounding environment and adapt accordingly. "Our current robots work well in structured environments that don't change. That sounds complex – but they're all the same," Dr Brinkworth said. "When cars are set on an assembly line, all the cars are the same, the parts are the same, they're all in the same environment, same lighting, same size. "But real life isn't like that – there's always changes, so we need to build robots that can adapt to the environment, rather than forcing the environment to adapt to the robots." Dr Russell Brinkworth studied the biology of insects to create a robot that can understand the vision it captures from a camera. (ABC News: Michael Clements) By studying the biology of insects, such as flies and dragonflies, Dr Brinkworth "reverse engineered" the process that goes on biologically to create a robot that can understand the vision it is capturing from a camera. "The way that insects interpret the world is very similar to the way primates and even humans interpret the world. We just do it on a larger scale," he said. He said his robots can pinpoint a drone or surveillance balloon from kilometres away, far advancing the technology of current cameras. "If you look at security footage and there's someone wearing a hoodie and they've got their face covered – it's hard to understand what's going on based on the camera footage," he said. "But if you were standing there, and you were looking at them, that shadow wouldn't fool your eye because your eye is sophisticated enough to be able to extract out the information hiding in the shadows. "That's the sort of cameras we're building." The robot can understand the vision it captures from a camera, far advancing the technology of current cameras. (:ABC News) Dr Brinkworth said the cameras would be vital for law enforcement, defence and national security in the future, but could also be used for conservation, such as recording the density of koalas in a national park. "You'd have to send somebody out to walk through that forest to do a manual count, or you send a drone overhead with an operator," he said. "They will miss quite a few. "What we've been able to do is design a system that is far more accurate and doesn't rely on human judgement." He said his camera can more accurately pick out where the koalas are because it could "break the different types of camouflage" created by the dense branches and leaves. "It can find animals hiding in forests, it can find all sorts of things that are camouflaged to regular camera views because it's able to look for, and enhance, the very subtle differences across different wavelengths of light." Dr Malcolm Davis says we need robots to be able to understand their environment and make their own decisions. (ABC News: Nick Haggarty) Australian Strategic Policy Institute senior analyst Dr Malcolm Davis said studying biology was important to advance technology such as robots. "We want them to be flexible and in effect, to be able to operate as humans do," he said. "The battle space is a complex, rapidly changing environment – we can't afford to have autonomous systems or robots, essentially requiring human oversight. "We need them to be able to understand their environment and make their own decisions. "There's obviously a Terminator mindset here that if we give robots too much control, then they turn on us [but] I don't think that's likely. "If we're talking about artificial intelligence, if we're talking about advanced robotic systems, then it's humans that are creating those systems. "We would build into those machines an understanding or basis for behaviour based around our approaches to things like international law, humanitarian law, the laws of armed conflict." Dr Brinkworth says the future applications of the robot are in science fiction territory. (CBS Interactive) Dr Brinkworth said the technology could one day be used to potentially build an eye for someone who was blind. "If somebody was blind, you can't just put a camera on them, you have to find a way of giving that information to the person," he said. He said in order to do that, the way the brain communicates needs to be replicated by better understanding how the visual systems of animals work. "We could augment existing visual systems and replace missing parts and communicate that information in the brain's native language back to the brain," he said. "This is very science fiction – it doesn't exist just yet. The interface between technology and biology is a frontier. "The augmentation side of technology moving into biology is a bit Borg-like, if we're looking for Star Trek. "It is very far in the future. But the future starts now, you must start somewhere. "If you want to invest in a future where giving sight back to the blind is important, than you've got to start." 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From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Discover the engineering revolution transforming modern defense with Strength, Stealth, Speed: The Very Fast Future of Advanced Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Future of Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Experimental platform connects full loop: design, simulation, data, identification, training, and real control. pipeline. Humanoid robots remain out of reach for most people due to their high cost, often priced higher than a car. Now, a new open-source project called LeRobot Humanoid by Hugging Face aims to change that with a low-cost, 3D-printed design built for learning and experimentation. While the current version focuses only on robotic legs, the platform offers an affordable entry point into humanoid robotics development. Priced at around $2,500, the system is designed for hobbyists, researchers, and developers looking to explore advanced robotics without the massive financial barrier typically associated with humanoid machines. In April 2025, New York-based Hugging Face entered hardware for the first time by acquiring the French startup Pollen Robotics, creators of the open-source humanoid robot Reachy 2. LeRobot is built as a modular bipedal robot platform using 3D-printed mechanical components, off-the-shelf hardware, and affordable actuators and electronics. The current version costs roughly $2,500 in parts, significantly lower than most humanoid systems used in robotics laboratories. Rather than focusing on polished consumer-grade robotics, the platform is designed for experimentation, rapid iteration, and accessibility. The hardware package includes printable mechanical files, a complete bill of materials, assembly instructions, wiring documentation, and motor setup tools. Structural components can be easily reprinted and replaced, allowing developers to test design modifications without rebuilding the entire system. This approach enables faster hardware iteration and makes the robot more practical for open-source research environments, according to the firm’s website. The robot currently focuses on lower-body locomotion, functioning primarily as a humanoid biped platform. While upper-body integration and more advanced whole-body manipulation are part of the future roadmap, the existing system already supports standing, walking experiments, calibration, and locomotion policy testing. The project also introduces a control-oriented design workflow. Instead of beginning with detailed CAD geometry, developers use simplified robot representations to evaluate design concepts through benchmark control tasks and optimal-control simulations. These lightweight models make it easier to compare mechanisms, validate balance strategies, and optimize motion performance before committing to physical hardware production. Once the robot is assembled, real-world datasets generated from the physical platform can be replayed in simulation to improve model accuracy. This identification pipeline helps reduce the sim-to-real gap by fitting simulator parameters based on actual robot behavior. As a result, simulation environments more accurately reflect real-world hardware performance, improving policy transfer reliability. LeRobot is designed as a complete robot-learning platform instead of just a humanoid robot. The project combines hardware, simulation, software tools, and training systems into one ecosystem that supports the full robotics development process. The platform includes a runtime stack that works with both simulated and real robots. It provides tools for calibration, monitoring robot states, sending commands, and performing safety checks. This allows developers to safely test robot controllers in simulation before using them on physical hardware, reducing the risk of damaging the robot during experiments. For AI and locomotion research, the robot is integrated into the LeRobot-legged-zoo AI framework, which includes open-source MJLab simulation environments. These environments help researchers train and test reinforcement learning policies for walking and movement tasks. Developers can also compare performance across different legged robot platforms. According to Hugging Face, a key feature of the project is its sim-to-real workflow. Real-world robot data can be replayed in simulation to identify differences between virtual and physical behavior. Engineers can then adjust simulator settings to improve accuracy and make trained policies transfer more reliably to real hardware. The release includes hardware files, design tools, runtime software, identification pipelines, and training environments. Although still experimental, the platform aims to make learning for humanoid robots more affordable, open, and accessible to researchers, developers, and robotics enthusiasts. Jijo is an automotive and business journalist based in India. Armed with a BA in History (Honors) from St. Stephen's College, Delhi University, and a PG diploma in Journalism from the Indian Institute of Mass Communication, Delhi, he has worked for news agencies, national newspapers, and automotive magazines. In his spare time, he likes to go off-roading, engage in political discourse, travel, and teach languages. Premium Follow

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Nachrichten, Termine, Experten d Instanz: Teilen Teilen: 27.05.2026 12:45 Despite significant progress, humanoid robots still face limitations in real-world working environments, especially when dealing with complex and rapidly changing situations. The “RobOdin” research project brings together AI and robotics expertise from Germany and Denmark to develop a new generation of humanoid assistance systems that support people in their everyday working lives in an intuitive, context-sensitive, and safe manner. The German Research Center for Artificial Intelligence (DFKI) in Lübeck is contributing an AI-based decision-making framework that enables intelligent, situation-dependent actions. In RobOdin, universities, research institutions and industry partners from northern Germany and southern Denmark are working together on high-performance humanoid robots for industrial and social applications. The aim is to create robust and practical systems that take on dangerous, monotonous or physically demanding tasks, thereby making work processes safer and more efficient. A particular focus is on interactive and social skills for natural and context-appropriate human-machine interaction. The systems are designed to recognise people, understand language, communicate naturally and react flexibly to dynamic situations. The project is led by the University of Southern Denmark. Other participants include the DFKI Laboratory in Lübeck, Flensburg University of Applied Sciences, the University of Lübeck and Christian-Albrechts-Universität zu Kiel. Industry and network partners include HARTING, Novo Nordisk, Diakonie Nord Nord Ost, the Schleswig-Flensburg Hazardous Materials Fire Brigade, navel robotics and the University of Twente. DFKI Lübeck: AI-based decision-making framework for humanoid robots The AI for Assistive Health Technologies research department at the DFKI Lübeck Laboratory is developing RobOdin, a customisable decision-making framework for humanoid robots. The system, which is based on large language models, integrates the outputs of various AI modules – Including object and emotion recognition – and consolidates them into a shared knowledge base. The model not only encompasses a general understanding of tasks for everyday robotic activities, but also takes into account the specific anatomy and interaction capabilities of the humanoid robot. Based on this, it processes the information and derives context-dependent action strategies for the robot. Standardised interfaces and data formats ensure consistent integration and seamless exchange between the components. This enables the robot to adapt its behaviour flexibly to different tasks and operating environments, and to continuously refine its capabilities. “For humanoid robots to collaborate meaningfully with humans in the future, they need a comprehensive understanding of their environment and the situation at hand,” says project leader Philipp Koch from the DFKI Laboratory in Lübeck. “Our aim is to combine AI models, sensor data, and speech processing in such a way that flexible and transparent decisions can be made.” Technological contributions from the partners The other project partners are also contributing key technological building blocks to the overall system: Christian-Albrechts-Universität zu Kiel is developing methods for visual perception and social navigation that enable robots to recognise and interpret gestures, objects and dynamic environments in real time. Building on this, the University of Lübeck is researching cognitive abilities such as situational understanding, task prediction and explainable decision-making models for humanoid robots, enabling a context-dependent interpretation of the environment. The University of Southern Denmark (SDU) is realising the humanoid robot platform. A key focus is on the sensor hands developed in collaboration with Harting, whose sensor technology – specifically designed for the fingers – is intended to enable fine motor tasks. In addition, SDU is integrating the AI and robotics components developed by the partners into a fully functional overall system. Flensburg University of Applied Sciences is designing a simulation and training environment as a digital twin, which enables the testing and optimisation of the AI components before deployment on real hardware. Tests in real-world scenarios Another key focus of the project is testing the humanoid robot in realistic application scenarios from industry, medicine and emergency response, including assembly processes, pharmaceutical laboratory applications and safety-critical operations. In collaboration with the Schleswig-Flensburg hazardous materials fire brigade, specific use cases such as the reconnaissance of hazardous situations, assistance with evacuations and the support of affected individuals are being investigated. HARTING is testing applications in assembly and manufacturing processes, whilst Novo Nordisk is providing a laboratory environment for pharmaceutical procedures. In collaboration with Diakonie Nord Nord Ost, social and assistive applications are also being researched. Building a German–Danish robotics network Furthermore, the project aims to establish a long-term network for robotics and AI research in southern Denmark and northern Germany. By pooling expertise from academia and industry, the project aims to create new synergies and sustainably strengthen regional innovation capacity. RoBodin is funded by the European Union through the Germany–Denmark Interreg program (Interreg 6A) from 1 October 2025 to 30 September 2028 with a total budget of €2,369,227.52. DFKI press contact: Swantje Schmidt German Research Center for Artificial Intelligence (DFKI) Communications & Media Bremen/Lübeck Mail: communications-hb@dfki.de Phone: +49 421 178 45 4121 DFKI project contact: Natascha Koch, M.Sc. German Research Center for Artificial Intelligence (DFKI) AI for Assistive Health Technologies Mail: natascha.koch@dfki.de Phone: +49 152 044 28821 Project coordinator: Oskar Palinko, Associate Professor University of Southern Denmark SDU Robotics Mail: ospa@mmmi.sdu.dk Phone: +45 655 082 11 https://robodin.eu/ project websitehttps://cloud.dfki.de/owncloud/index.php/s/dt62C8Hedd6RSK3 Image material can be downloaded here - The copyright notice must be included when using the photos. <The initial version of the robotic platform developed by the University of Southern Denmark as part ...Quelle: SDU, Oskar ParlinkoCopyright: SDU, Oskar Parlinko <Screenshot from the simulated 3D training environment developed by Flensburg University of Applied S ...Quelle: FH FlensburgCopyright: FH Flensburg Merkmale dieser Pressemitteilung: Journalisten Informationstechnik überregional Forschungsprojekte, Kooperationen Englisch <The initial version of the robotic platform developed by the University of Southern Denmark as part ...Quelle: SDU, Oskar ParlinkoCopyright: SDU, Oskar Parlinko <Screenshot from the simulated 3D training environment developed by Flensburg University of Applied S ...Quelle: FH FlensburgCopyright: FH Flensburg Zum Download Zum Download Suche in Pressemitteilungen Suche in Terminen Anfangsdatum Enddatum Sie können Suchbegriffe mit und, oder und / oder nicht verknüpfen, z. 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ITHACA, N.Y. – In the quiet, controlled environment of a Cornell University laboratory, a new form of life is taking its first steps. Invisible to the naked eye and smaller than a single grain of salt, these microscopic machines are not biological. They are fully autonomous robots, complete with onboard electronic brains, actuators for legs, and photovoltaic cells that serve as a rudimentary metabolism, converting light into the electricity that powers their existence. This is not science fiction; it is the tangible result of a decade-long quest to shrink robotics down to the cellular scale. For years, the field of microrobotics has been dominated by devices that were either tethered by wires for power and control or manipulated externally by magnetic fields, limiting their utility in complex, enclosed environments like the human body. The Cornell team, led by physicist Paul McEuen and researcher Itai Cohen, has shattered that paradigm. By leveraging the same semiconductor manufacturing technology that builds billions of transistors on a computer chip, they have created a fleet of robots, each about 100 microns wide, that operate with unprecedented independence. “This is the first time we’ve been able to build, in a very standard, scalable way, an autonomous robot at this scale,” Professor Cohen explained in an interview with Wired. A Leap in Miniaturization Bypasses Tethers and External Fields The core innovation lies in the integration of complementary metal-oxide-semiconductor (CMOS) electronics—the bedrock of the modern digital world—directly onto the robot’s chassis. This allows each microbot to carry its own control system, a simple circuit that functions as a clock, coordinating the firing of its ‘legs’. As detailed in their foundational paper published in the journal Nature, this onboard brain enables the robot to execute pre-programmed commands without any external guidance. The robot’s body is essentially a silicon wafer, meticulously etched and layered to house both the control circuit and the solar cells that power it. This self-sufficiency is a monumental step forward. Previous micro-scale machines were often simple particles of iron oxide pulled through fluid by powerful, external magnets. While effective for simple tasks, this approach is akin to puppetry, with the device having no agency of its own. The Cornell robots, by contrast, carry their instructions internally. A laser beam, acting as a power source, illuminates the photovoltaic cells on the robot’s back, and the onboard circuit directs that energy to its actuators, initiating movement. This untethered freedom opens the door to navigating intricate, previously inaccessible environments, from the tangled pipe networks of a microfluidic chip to the delicate capillaries of living tissue. Harnessing Light and Bubbles for Untethered Propulsion The method of propulsion is as innovative as the electronics. The robot’s legs are not mechanical joints but tiny strips of platinum, only a few atoms thick, layered over titanium or graphene. When the onboard circuit sends a small electrical current to a platinum leg, it triggers an electrochemical reaction with the surrounding water, splitting it and creating a minuscule bubble of hydrogen and oxygen. The formation and subsequent collapse of this bubble generates a thrust, pushing the leg and propelling the robot forward. By alternating the current between its front and back legs, the robot can clumsily ‘swim’ or crawl. This bubble-based actuation is remarkably efficient at this scale, but it is also difficult to control with precision. The chaotic nature of bubble formation makes smooth, directed movement a significant challenge. Early versions of the robots were programmed with simple, alternating signals, resulting in locomotion that was functional but erratic. The researchers knew that to unlock the robots’ true potential, especially for delicate tasks like targeted drug delivery or microsurgery, they needed to teach them to walk with purpose and control. The solution, it turned out, would come not from better hardware, but from smarter software. From Rudimentary Actuation to AI-Driven Locomotion In a significant advancement announced in early 2023, the Cornell team revealed they had successfully used artificial intelligence to teach their microscopic robots how to walk. As reported by the Cornell Chronicle, the researchers developed a computer simulation of the robot and its bubble-propulsion physics. They then unleashed a reinforcement learning algorithm on the simulation, allowing the AI to experiment with countless different sequences of leg activations to discover which patterns, or ‘gaits’, produced the most effective and efficient movement. This AI-driven approach bypassed the painstaking process of human trial and error. The AI discovered several gaits that were far more effective than those a human engineer might have programmed. By varying the timing and intensity of the bubble generation, the AI learned how to make the robot walk faster and more directly toward a target. “The AI was able to find gaits that were more clever and effective than we could have imagined,” said Michael Reynolds, a former student in Cohen’s lab. These optimized commands are then translated into a program and flashed onto the robot’s onboard CMOS circuit, giving the physical machine a new, AI-honed ability. According to New Scientist, this method allows the robots to achieve speeds of more than 10 micrometers per second, a significant velocity for a machine of its size. Charting a Course for In-Vivo Diagnostics and Micro-Manufacturing The long-term vision for these autonomous microbots is transformative. Researchers envision swarms of these devices being injected into the bloodstream to patrol for cancer cells, deliver drugs directly to a tumor, or clear plaque from arteries. Their tiny size would allow them to perform diagnostics at a cellular level, a concept known as ‘in-vivo’ diagnostics. In the materials science and manufacturing sectors, they could be used to assemble microscopic structures, repair microelectronic circuits from the inside, or serve as mobile sensors in industrial chemical vats or sensitive environmental sites. However, significant hurdles remain before these applications become reality. The current robots are powered by external lasers, which cannot penetrate deep into opaque materials like human tissue. Future versions will need more sophisticated onboard power storage or the ability to harvest energy from their local environment, such as chemical gradients or thermal energy. Furthermore, navigating the turbulent, crowded environment of the bloodstream is infinitely more complex than a petri dish. The robots will need advanced sensors and more sophisticated onboard intelligence to orient themselves and respond to their surroundings. Biocompatibility is another critical challenge; the materials must not trigger an immune response, and the bubble-propulsion system must be proven safe for use in living organisms. The Path to Mass Production and Broader Intelligence Despite these challenges, the project’s foundation in standard semiconductor fabrication is perhaps its most powerful asset. Because the robots are made on silicon wafers, they can be produced in parallel by the thousand, or even million, in a single batch. This scalability is crucial for creating the large swarms needed for many proposed applications and drastically reduces the cost per unit, moving microrobotics from a bespoke laboratory curiosity toward a mass-producible technology. The team is now working to integrate more complex sensors onto the robot’s chassis, which could detect temperature, chemicals, or specific biological markers. The integration of AI marks a pivotal moment, transforming the devices from simple automatons into adaptable agents capable of learning. The next phase of research will focus on giving the robots the ability to make decisions based on sensory input—to change direction if an obstacle is detected or to release a payload when a specific chemical signature is found. As the onboard circuits become more complex and the AI training becomes more sophisticated, these salt-sized explorers are poised to unlock a new era of technology, one that operates on a scale previously confined to the domain of biology itself. ITHACA, N.Y. – In the quiet, controlled environment of a Cornell University laboratory, a new form of life is taking its first steps. Invisible to the naked eye and smaller than a single grain of salt, these microscopic machines are not biological. They are fully autonomous robots, complete with onboard electronic brains, actuators for legs, and photovoltaic cells that serve as a rudimentary metabolism, converting light into the electricity that powers their existence. This is not science fiction; it is the tangible result of a decade-long quest to shrink robotics down to the cellular scale. For years, the field of microrobotics has been dominated by devices that were either tethered by wires for power and control or manipulated externally by magnetic fields, limiting their utility in complex, enclosed environments like the human body. The Cornell team, led by physicist Paul McEuen and researcher Itai Cohen, has shattered that paradigm. By leveraging the same semiconductor manufacturing technology that builds billions of transistors on a computer chip, they have created a fleet of robots, each about 100 microns wide, that operate with unprecedented independence. “This is the first time we’ve been able to build, in a very standard, scalable way, an autonomous robot at this scale,” Professor Cohen explained in an interview with Wired. A Leap in Miniaturization Bypasses Tethers and External Fields The core innovation lies in the integration of complementary metal-oxide-semiconductor (CMOS) electronics—the bedrock of the modern digital world—directly onto the robot’s chassis. This allows each microbot to carry its own control system, a simple circuit that functions as a clock, coordinating the firing of its ‘legs’. As detailed in their foundational paper published in the journal Nature, this onboard brain enables the robot to execute pre-programmed commands without any external guidance. The robot’s body is essentially a silicon wafer, meticulously etched and layered to house both the control circuit and the solar cells that power it. This self-sufficiency is a monumental step forward. Previous micro-scale machines were often simple particles of iron oxide pulled through fluid by powerful, external magnets. While effective for simple tasks, this approach is akin to puppetry, with the device having no agency of its own. The Cornell robots, by contrast, carry their instructions internally. A laser beam, acting as a power source, illuminates the photovoltaic cells on the robot’s back, and the onboard circuit directs that energy to its actuators, initiating movement. This untethered freedom opens the door to navigating intricate, previously inaccessible environments, from the tangled pipe networks of a microfluidic chip to the delicate capillaries of living tissue. Harnessing Light and Bubbles for Untethered Propulsion The method of propulsion is as innovative as the electronics. The robot’s legs are not mechanical joints but tiny strips of platinum, only a few atoms thick, layered over titanium or graphene. When the onboard circuit sends a small electrical current to a platinum leg, it triggers an electrochemical reaction with the surrounding water, splitting it and creating a minuscule bubble of hydrogen and oxygen. The formation and subsequent collapse of this bubble generates a thrust, pushing the leg and propelling the robot forward. By alternating the current between its front and back legs, the robot can clumsily ‘swim’ or crawl. This bubble-based actuation is remarkably efficient at this scale, but it is also difficult to control with precision. The chaotic nature of bubble formation makes smooth, directed movement a significant challenge. Early versions of the robots were programmed with simple, alternating signals, resulting in locomotion that was functional but erratic. The researchers knew that to unlock the robots’ true potential, especially for delicate tasks like targeted drug delivery or microsurgery, they needed to teach them to walk with purpose and control. The solution, it turned out, would come not from better hardware, but from smarter software. From Rudimentary Actuation to AI-Driven Locomotion In a significant advancement announced in early 2023, the Cornell team revealed they had successfully used artificial intelligence to teach their microscopic robots how to walk. As reported by the Cornell Chronicle, the researchers developed a computer simulation of the robot and its bubble-propulsion physics. They then unleashed a reinforcement learning algorithm on the simulation, allowing the AI to experiment with countless different sequences of leg activations to discover which patterns, or ‘gaits’, produced the most effective and efficient movement. This AI-driven approach bypassed the painstaking process of human trial and error. The AI discovered several gaits that were far more effective than those a human engineer might have programmed. By varying the timing and intensity of the bubble generation, the AI learned how to make the robot walk faster and more directly toward a target. “The AI was able to find gaits that were more clever and effective than we could have imagined,” said Michael Reynolds, a former student in Cohen’s lab. These optimized commands are then translated into a program and flashed onto the robot’s onboard CMOS circuit, giving the physical machine a new, AI-honed ability. According to New Scientist, this method allows the robots to achieve speeds of more than 10 micrometers per second, a significant velocity for a machine of its size. Charting a Course for In-Vivo Diagnostics and Micro-Manufacturing The long-term vision for these autonomous microbots is transformative. Researchers envision swarms of these devices being injected into the bloodstream to patrol for cancer cells, deliver drugs directly to a tumor, or clear plaque from arteries. Their tiny size would allow them to perform diagnostics at a cellular level, a concept known as ‘in-vivo’ diagnostics. In the materials science and manufacturing sectors, they could be used to assemble microscopic structures, repair microelectronic circuits from the inside, or serve as mobile sensors in industrial chemical vats or sensitive environmental sites. However, significant hurdles remain before these applications become reality. The current robots are powered by external lasers, which cannot penetrate deep into opaque materials like human tissue. Future versions will need more sophisticated onboard power storage or the ability to harvest energy from their local environment, such as chemical gradients or thermal energy. Furthermore, navigating the turbulent, crowded environment of the bloodstream is infinitely more complex than a petri dish. The robots will need advanced sensors and more sophisticated onboard intelligence to orient themselves and respond to their surroundings. Biocompatibility is another critical challenge; the materials must not trigger an immune response, and the bubble-propulsion system must be proven safe for use in living organisms. The Path to Mass Production and Broader Intelligence Despite these challenges, the project’s foundation in standard semiconductor fabrication is perhaps its most powerful asset. Because the robots are made on silicon wafers, they can be produced in parallel by the thousand, or even million, in a single batch. This scalability is crucial for creating the large swarms needed for many proposed applications and drastically reduces the cost per unit, moving microrobotics from a bespoke laboratory curiosity toward a mass-producible technology. The team is now working to integrate more complex sensors onto the robot’s chassis, which could detect temperature, chemicals, or specific biological markers. The integration of AI marks a pivotal moment, transforming the devices from simple automatons into adaptable agents capable of learning. The next phase of research will focus on giving the robots the ability to make decisions based on sensory input—to change direction if an obstacle is detected or to release a payload when a specific chemical signature is found. As the onboard circuits become more complex and the AI training becomes more sophisticated, these salt-sized explorers are poised to unlock a new era of technology, one that operates on a scale previously confined to the domain of biology itself. Subscribe for Updates Help us improve our content by reporting any issues you find. Get the free daily newsletter read by decision makers Get our media kit WebProNews is a leading publisher of business and technology email newsletters and websites. Deliver your marketing message directly to decision makers.

Geeky Gadgets The Latest Technology News 12:15 pm May 19, 2026 By Julian Horsey Boston Dynamics offers a detailed examination of Atlas, a humanoid robot engineered to perform intricate physical tasks. Atlas’s development relies on a combination of virtual simulations and real-world testing to refine its movements and decision-making. For example, engineers simulate new behaviors in controlled environments to assess their feasibility and safety before applying them to the physical robot. This approach highlights the importance of iterative testing in creating a system capable of handling complex challenges. Explore how structured testing processes improve Atlas’s ability to adapt to unpredictable environments. Learn about the specific hurdles involved in designing a robot to navigate uneven terrain and perform dynamic actions. The overview also provide more insights into potential real-world uses for Atlas, such as disaster response and physically intensive industrial tasks. TL;DR Key Takeaways : The design of Atlas reflects a deliberate balance between complexity and simplicity. While the robot incorporates highly sophisticated systems, its construction emphasizes reliability, ease of maintenance and cost-efficiency. This pragmatic approach ensures that Atlas remains adaptable, allowing engineers to introduce new features or refine existing ones without compromising its core functionality. By prioritizing robust engineering principles, Atlas is equipped to handle demanding tasks while serving as a flexible platform for ongoing development. This focus on simplicity and functionality ensures that Atlas is not only a technological marvel but also a practical tool for real-world challenges. Atlas’s physical capabilities are a testament to the advancements in robotics. Its design enables full-body motion, agility and strength, allowing it to perform tasks that require precise coordination and significant power. For example, Atlas has demonstrated its ability to lift and carry heavy objects, such as a 100-pound refrigerator, with remarkable ease. Its ability to bend, twist and balance mirrors human movements, making it a versatile tool for tasks that demand both strength and finesse. These capabilities position Atlas as a potential solution for environments where human-like dexterity and power are essential, such as disaster zones or industrial settings. Become an expert in Boston Dynamics with the help of our in-depth articles and helpful guides. Boston Dynamics employs rigorous testing protocols to refine Atlas’s abilities. These tests simulate real-world challenges, such as navigating uneven terrain, manipulating heavy objects, or performing tasks in dynamic environments. By exposing Atlas to these scenarios, the team gathers critical data to improve its control systems and behavior algorithms. This iterative process ensures that Atlas can adapt and perform reliably in unpredictable conditions. The emphasis on real-world testing not only validates the robot’s physical capabilities but also highlights its potential for practical applications outside the lab. A key aspect of Atlas’s development is its seamless transition from virtual simulations to real-world applications. Boston Dynamics utilizes an advanced testing pipeline that allows engineers to train and evaluate new behaviors in a simulated environment before deploying them to the physical robot. This approach minimizes risks, accelerates development and ensures that Atlas performs efficiently when faced with real-world challenges. By fine-tuning behaviors in simulation, the team can address potential issues early in the process, resulting in a more reliable and capable robot. This method underscores the importance of combining virtual and physical testing to achieve optimal performance. Boston Dynamics is committed to expanding the capabilities of humanoid robots like Atlas. The team continuously explores new tasks and challenges, pushing the limits of what Atlas can achieve. Their ultimate goal is to prepare Atlas for practical applications beyond the lab, such as disaster response, industrial automation and other scenarios where human-like robots could provide significant value. This relentless pursuit of innovation positions Atlas as a key player in the future of robotics, bridging the gap between research prototypes and functional tools for real-world use. The potential applications for Atlas extend far beyond research and development. Boston Dynamics envisions a future where humanoid robots assist in industries that require repetitive, dangerous, or physically demanding tasks. For instance, Atlas could play a critical role in emergency response, helping rescue workers navigate hazardous environments or carry out search-and-rescue missions. In manufacturing, Atlas’s strength and precision could streamline processes that are currently labor-intensive or unsafe for humans. By combining advanced technology with creative problem-solving, Boston Dynamics aims to transform Atlas from a research prototype into a practical tool that addresses real-world challenges. This vision highlights the robot’s potential to transform industries and improve efficiency and safety across various fields. Atlas’s journey from concept to reality demonstrates the power of innovation and engineering excellence. By focusing on reliability, cost-efficiency and advanced capabilities, Boston Dynamics has created a humanoid robot that sets a new standard for the field. As the team continues to refine and expand Atlas’s abilities, the possibilities for its real-world applications grow increasingly promising. Atlas is not just a technological achievement; it represents a bold step toward a future where humanoid robots play an integral role in solving complex challenges and enhancing human capabilities. Media Credit: Boston Dynamics Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

Recibí notificaciones de las noticias más relevantes del Portal Suscribite GRATIS a nuestro canal de WhatsApp y recibí las noticias en tu celular Recibí notificaciones de las noticias más relevantes del Portal Suscribite GRATIS a nuestro canal de WhatsApp y recibí las noticias en tu celular Foto: Shutterstock. Escuchar nota Generando audio… 28.02.2025 19:19 Lectura: 2' Montevideo Portal Expertos en seguridad de XLab, una compañía de tecnologías de la información (TI), descubrió una nueva variante de Vo1d, una botnet conocida desde hace mucho tiempo que infecta dispositivos Android TV. Según consigna el portal PCWorld, la nueva variante infectó a más de 1,6 millones de dispositivos Android TV en todo el mundo. De esta manera, los convirtió en robots de malware controlados de forma remota. Según los investigadores de seguridad, la nueva variante Vo1d (o más bien la botnet basada en ella) se protege a sí misma con un cifrado mejorado (que evita que los expertos en ciberseguridad envíen comandos a los bots y los analicen) y capacidades de encubrimiento mejoradas. Los robots de Android TV infectados son luego reclutados por el servidor de comando y control para actividades ilegales, incluidos ataques DDoS (en los que un grupo de robots desactiva un servicio inundándolo con solicitudes) y fraude de clics en anuncios (en el que los robots imitan a los usuarios simulando clics en anuncios y generando ingresos para los anunciantes fraudulentos), añade el citado medio. La botnet Vo1d es una de las más grandes de los últimos años. Si bien Vo1d está activo en todo el mundo, la mayoría de los informes de infecciones provienen de Argentina, Brasil, China, Indonesia, Sudáfrica y Tailandia. ¿Cómo protegerte? El primer paso es comprar un dispositivo Android TV de una marca y tienda con buena reputación. El malware puede ser preinstalado en dispositivos Android TV, ya sea por el fabricante del dispositivo o introducido por un intermediario a lo largo de la cadena de producción. La siguiente medida defensiva más importante es instalar todas las actualizaciones de seguridad y de firmware publicadas por el fabricante del dispositivo Android TV y mantenerse al día con las actualizaciones futuras. Esto evitará que los atacantes infecten su dispositivo de forma remota a través de vulnerabilidades de seguridad, recomendó PCWorld. Finalmente, como tercera medida de seguridad, es importante que solo se instalen aplicaciones desde la Play Store y no desde ninguna otra tienda de aplicaciones. Montevideo Portal Suscribite GRATIS a nuestro canal de WhatsApp y recibí las noticias en tu celular Esto es para poder mejorar el intercambio entre los usuarios y que sea un lugar que respete las normas de convivencia. A su vez, habilitamos la casilla [email protected], para que los lectores puedan reportar comentarios que consideren fuera de lugar y que rompan las normas de convivencia. Para comentar, te pedimos que confirmes tu identidad ingresando tu celular. Te enviaremos un código de verificación vía WhatsApp. Para comentar, te pedimos que agregues una imagen de perfil. O elegí una de nuestras ilustraciones: Ocurrió un error al guardar la imagen seleccionada, ingresa aquí para agregar la imagen.

Add the following snippet to your HTML:<iframe frameborder='0' height='385' scrolling='no' src='https://www.hackster.io/quartz-components/ai-based-text-to-speech-system-with-max98357a-esp32-c11432/embed' width='350'></iframe> Build a real-time AI powered Text-to-Speech system using ESP32 and MAX98357A I2S amplifier for smart voice and IoT projects. Read up about this project on Build a real-time AI powered Text-to-Speech system using ESP32 and MAX98357A I2S amplifier for smart voice and IoT projects. Build a real-time AI powered text to voice converter using ESP32 Development Board,MAX98357A I2S Audio Amplifier, and the WitAITTS Library. This project connects the ESP32 to the Wit.ai cloud platform through WiFi and converts typed text into natural sounding speech in real time. The system supports multiple voice characters including male, female, pirate, wizard, cartoon, vampire, and British butler style voices. Audio output is streamed directly through the MAX98357A amplifier and speaker using the ESP32 I2S interface. The project demonstrates practical implementation of cloud-based AI speech synthesis, WiFi communication, I2S digital audio streaming, Serial Monitor interaction, and multi-voice text-to-speech generation using embedded hardware. The WitAITTS library is required for WiFi communication, cloud-based speech synthesis, and I2S audio streaming on the ESP32. Install the library before uploading the project code. Fig. Installing WitAITTS Library in Arduino IDE The ESP32 requires a Wit.ai API token to access the cloud-based text-to-speech service. The token can be generated from the Wit.ai developer dashboard. Arduino · C++ Fig. Generating Wit.ai API Token The ESP32 connects to the internet using WiFi and communicates with the Wit.ai cloud platform through the WitAITTS library. Text entered through the Serial Monitor is converted into speech audio using multiple selectable AI voice characters. The generated digital audio stream is sent through the ESP32 I2S interface to the MAX98357A amplifier module, which drives the speaker for real-time voice output. Checkout the full tutorial: Hackster.io, an Avnet Community © 2026

When news breaks, you need to understand what actually matters — and what to do about it. At Vox, our mission to help you make sense of the world has never been more vital. But we can’t do it on our own. We rely on readers like you to fund our journalism. Will you support our work and become a Vox Member today? AI is making them better — but they’re not going to be doing your chores anytime soon. Humanoid robots have been everywhere lately. They’re running half-marathons in Beijing. They’re chasing wild boars off the streets of Warsaw. They’re getting put to work as airport baggage handlers, waste sorters, and traffic cops. They’re walking the red carpet with first lady Melania Trump at the White House. They’re even being ordained as Buddhist monks. Humanoid robots have been hyped as the future of everything, from completing household chores to caring for elders to doing the dirty work on the factory floor, while Elon Musk is pivoting Tesla from cars to humanoid robots, claiming they’ll soon outnumber humans. Today, Explained host Sean Rameswaram talked to tech writer and journalist James Vincent — who wrote a Harper’s Magazine cover story titled “Kicking Robots” — about the humanoid robot hype and how much of its promise can actually be realized. Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify. James, you’ve had the distinct privilege of doing something most of us still haven’t done — you got to meet a bunch of robots. How many robots did you meet? I lost count after the first few, I’ll be honest. I met a few from two of the leading companies in the US. One is called Apptronik and another is called Agility Robotics. They make two very different styles of robot. They’re both humanoids in that they resemble a human — arms, legs, etc. — but Agility is very much focused on the warehouse and their robots look a little bit more inhuman. They have those backward-facing knees. Apptronik makes a more general purpose robot that looks much more like a human in terms of normal body proportion, it stands upright, and you look it eye to eye — or eye to unblinking robot eye, whatever that might be. I got to meet them, shake hands. I played ick-ack-ock, as rock paper scissors is sometimes called in the UK. And I also — this was my heart’s content, I so wanted to do this — I wanted to kick a robot. I have that burning urge inside me that I want to get my own back before they obviously take over the world. So the robots were nice to you, but you weren’t that nice to them. Oh, I was horrible. I was terrible. They’re going to be coming for me in the future. I have no doubt about that at all. They didn’t actually let me kick a robot, I’m very sad to say. They said it might be a bit of a safety hazard, so I got to poke one very hard with a big stick instead. And that was the next best thing. Did it tip over? No, it didn’t. This was the creepy thing about it. They gave me this very high-tech stick, which was I think a broom handle with a bit of safety foam taped on the end of it. And they said, “Give it a shove, give it a punt. See how hard you can push it.” And I was very nervous about this because they told me that this was one of the prototype humanoids. It was worth hundreds of thousands of dollars. And if I knock it down and it breaks, that’s great copy, but it’s also the end of my access to this company. They’re not going to be pleased. I gave it a shove and it wobbled, and they were like, “No, you can do it harder than that.” I gave it as hard as I could. It staggered backwards and threw its arms up in the air as it regained its equilibrium. It was just such an uncanny moment to see a robot mimic so perfectly, to my eyes, the movements of a human. I remember doing this and having it stagger backwards and then trot back up to me, look me right in the face, and I was like, “Oh gosh, these things are real.” What are humanoid robots meant to do, James? If you believe the pitch decks and the hype men, they’re meant to do anything that an able-bodied human can do. They’re meant to slot right into the workplace, sort packages, bolt on car doors, anything and everything. This is the pitch. This is why they are built like humans. They want them to do anything that a human laborer can do. And that’s a big ask. Who’s asking the robots to do it all right now? A lot of companies in the US and in China, mainly. These are the two leaders in the robotics space. It used to be mainly startups, but now we’re seeing more of the big tech companies move into this space as well. Meta recently bought a robotic startup. Google has been doing stuff with robots for ages. It’s been testing its AI out on them. And Tesla — it’s Elon Musk’s obsession, alongside colonizing Mars. He thinks that Optimus, which is the name of Tesla’s robot, is going to be the most productive, the most profitable product ever invented. I think this is typical Muskian hyperbole. But his interest is something that has moved the market hugely. And when he got involved, a lot of companies followed suit. Why is it that we’re seeing more of this stuff? Is it just because there are more robots now? The big reason for why we’re having this moment for humanoids at the moment is AI. The ChatGPT boom and deep learning have enabled large language models or chatbots. A lot of people have thought that this is a transferable technology that we can plug into humanoid machines and other machines and it can learn in the same way that chatbots have been able to learn and to reproduce human speech. The big thing that they’re depending on is that robots in the past had to be programmed manually. You had to say, “Move your arm here, down this many degrees, across like this, and apply this much pressure.” What you have with the new form of AI is that it learns these lessons by itself. You plug in a lot of data, you give it an output that you want, and it learns how to connect those pieces together. These companies hope that if we get enough data, we will “solve the problem of physical robotics” and we will have these machines that are multidexterous and capable of all these different tasks. The big criticism of that is that robots are not in the same world as chatbots. Chatbots are dealing with text. You talk to a chatbot even today and it will still make mistakes every now and again. When those mistakes are transferred to the physical world, they suddenly become a lot more potentially dangerous. A big thing that a lot of companies are doing at the moment is they’re saying, “We’re going to put these robots in the home. They are going to be the perfect robot butler and they will take care of your dishes and your laundry and all the rest of it.” If a chatbot gets something wrong when you’re asking it to do some research, then it’s not the biggest deal in the world. You may spot the error and correct it. If a robot gets something wrong when it is cleaning away your plates and dishes, if it breaks one in every 10 cups, are you going to be happy with that quality? No, I don’t think so. Is the way China’s developing these machines different from the way we are? I would say that the main difference is that China’s doing it faster and better. I think there is more of a focus in the US on home products as a marketing tool to the rich and saying, “Look, we can take care of all these chores for you.” In China, you have what is one of the fastest aging populations in the world. People over 60 are predicted to be 30 percent of the population by 2040. So you have a loss of manufacturing labor and you have an increased burden on social care. I think for Chinese state planners, humanoid robotics could very much plug into both of those gaps at the same time. There is a slightly different focus, but it is one that is organic in terms of the advantages of the Chinese economy. The big thing that the Chinese economy has that the US doesn’t is scale. It has a massive ability to manufacture these units. It can make thousands at a time. This is why China is pulling ahead. You spent a lot of time in your piece trying to suss out the hype versus the reality. Where do you land? Is this going to be our reality within a few years or is this more like flying cars? I think it’s nearer to flying cars than it is to the chatbot side of things. We’ve seen really rapid advances. There has been a legitimate leap forward in terms of capabilities. However, that does not mean that we are matching the hype that is being pushed out by people like Elon Musk and other leading companies who are saying, “We’re going to have one of these robots in your house next year and it’s going to be doing all the chores you need and it’ll never make a mistake and it certainly won’t fall over and kill your cat.” I think those promises are just not true. I can see humanoid robots becoming a more common presence within both the work and the home over the next 10-plus years. But in the next five years, in the next three years, I really doubt it. Understand the world with a daily explainer, plus the most compelling stories of the day. 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From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Discover the engineering revolution transforming modern defense with Strength, Stealth, Speed: The Very Fast Future of Advanced Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Future of Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. The startup already has a star-studded lineup of investors, including Chinese tech giant Meituan. Chinese robotics company X Sqaure Robot has raised $143.3 million (1 billion yuan) in a Series A++ round backed by heavyweights ByteDance and HSG (formerly Sequoia Capital China). The group of investors also included government-backed investment firms, such as Beijing Information Industry Development Investment Fund, Shenzhen Capital Group (SCGC), Nanshan SEI Investment, and Wuxi Capital Group. Aiming for widespread use in homes, hotels, and logistics, the Chinese firm specializes in creating humanoid robots and general-purpose embodied AI. It is well-known for its Quanta X1/X2 wheeled humanoid models with dexterous hands. Founded in 2023, it is one of China’s most well-funded startups in the field of embodied intelligence development. The startup has developed its own vision–language–action (VLA) model, WALL-A, that combines VLA systems with world models to form an integrated architecture. By using world models to predict actions and causal reasoning to interpret feedback, the system improves a robot’s ability to generalize to new tasks without prior training. This approach allows robots to perform mobile manipulation more effectively in complex, unstructured environments. With this deal, X Square Robot has added ByteDance to a list of shareholders that already includes Chinese tech giants Meituan and Alibaba Group. Meituan has been a major backer of X Square Robot since its early days, leading and participating in the company’s initial financing rounds, including its Series A. Those early rounds raised several hundred million yuan. Meituan’s corporate venture capital (CVC), Dragon Ball Capital, is also included on the list of participants. Over time, Meituan’s total investment in X Square Robot has grown to more than ¥1 billion (about US$140 million), according to media reports. The Shenzhen-based startup has launched an array of products, including the general-purpose wheeled bimanual robot Quanta X1 and its successor, Quanta X2, as well as the dexterous hand model ArtiXon. Powered by X Square’s WALL-A embodied foundation model, Quanta X1 uses a wheeled chassis and lightweight robotic arms with a working range of up to 1 m. It operates at a maximum speed of 2 m/s and offers 20 degrees of freedom. On the other hand, Quanta X2 is backed by the full-stack WALL-A, a large operating model. It has a dual-arm payload of 55 lbs (25 kg), offering 62 degrees of freedom, and stands 5.5 feet (164 cm) tall. The robot features a 4-inch interactive screen that can display real-time facial expressions, a chest-mounted microphone array, and LLM-powered dialogue for more natural human interaction. Both Quanta X1 and X2 have been used to perform real-world autonomous tasks. The X Square dexterous hand is designed to mimic the human hand with precision. Having 20 degrees of freedom and 15 actuators, it can perform 31 different manipulation tasks, such as pinching, grasping, and twisting. It can open and close in under 1 second. X Square Robot revealed that its full stack of self-developed embodied AI software and hardware played a major role in cutting costs for building the AI robots. The company is now moving toward mass production and commercialization across multiple sectors, beginning with industrial manufacturing, logistics sorting, home-based elderly care, and commercial cleaning. Atharva is a full-time content writer with a post-graduate degree in media & amp; entertainment and a graduate degree in electronics & telecommunications. He has written in the sports and technology domains respectively. In his leisure time, Atharva loves learning about digital marketing and watching soccer matches. His main goal behind joining Interesting Engineering is to learn more about how the recent technological advancements are helping human beings on both societal and individual levels in their daily lives. Premium Follow

ByPaul Lamkin, Contributor. SwitchBot is officially launching its AI Pet companion robots, months after teasing the concept last year at IFA. The new KATA Friends range consists of Noa and Niko: soft-bodied AI-powered robot companions designed to wander around your house, react to touch, recognize emotions, remember routines, and apparently develop attachment patterns based on how you raise them. Yep… I said “raise them”. SwitchBot, who isn’t shy about pushing the boundaries when it comes to wacky smart home devices, says the robots can greet you at the door, respond differently to individual family members, sense mood changes through voice recognition and even keep a diary of shared experiences while taking photos “from their own perspective.” Under the fluffy exterior, there’s a fairly serious pile of AI tech. SwitchBot says the robots combine on-device LLM processing with local visual recognition for gestures and facial recognition, alongside cloud-based AI features for more advanced interaction. There are 12 touch-sensitive zones across the body, autonomous navigation, obstacle avoidance, and self-charging support. It’s essentially a Tamagotchi crossed with a home robot, wrapped around a ChatGPT-style conversational system. And somehow, it still isn’t the weirdest thing SwitchBot has shown off lately. The company has spent the past year repositioning itself from a smart home accessories brand into what it calls an “AI-enabled embodied home robotics” company. At IFA 2025, the KATA Friends reveal arrived alongside the company’s AI Hub, a control system with OpenClaw baked in, designed to make smart home automation more conversational and context-aware. Then there’s Acemate, the AI tennis robot incubated by SwitchBot, which is essentially a robotic tennis coach. There’s definitely a market forming around emotionally aware AI hardware. Devices like Rabbit’s R1, Humane’s ill-fated AI Pin, and the growing wave of AI companion apps have shown the tech industry is increasingly obsessed with making AI feel personal rather than purely functional. SwitchBot’s approach just happens to involve giving that idea googly eyes and the ability to follow you around the house… just don’t get it wet or feed it after midnight (I’m not sure the latter is possible). At $700, it’s a pretty big outlay for something that could, theoretically, become jealous and sulk. Buried underneath the cuteness (and silliness) is a more serious industry trend though, one shifting toward personality and contextual awareness. CES 2026 was packed with AI robots… it wouldn’t surprise me at all if more brands follow SwitchBot’s lead and make their next wave of robots more “friendly”. Noa and Niko can be purchased now, direct from SwitchBot.

From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Discover the engineering revolution transforming modern defense with Strength, Stealth, Speed: The Very Fast Future of Advanced Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Future of Defense Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Hyundai also plans 300,000+ US-made actuators yearly, key components powering robot joints and movement. Hyundai plans to deploy more than 25,000 Atlas humanoid robots developed by Boston Dynamics in the US. The announcement came during a session hosted by JPMorgan Chase, where the automaker outlined its broader robotics manufacturing strategy. Hyundai said it aims to build an annual production capacity of 30,000 Atlas robots by 2028 and plans to manufacture key robot components locally, signaling a major expansion of humanoid robotics in automotive production. Yesterday, Boston Dynamics showed its Atlas humanoid learned heavy-object handling through reinforcement learning, simulations, torso rotation, and adaptive balance control during transport. Hyundai Motor Group (HMG) plans to deploy 25,000 Atlas humanoid robots developed by its subsidiary Boston Dynamics across Hyundai Motor and Kia manufacturing facilities. The company aims to reach an annual production capacity of 30,000 Atlas robots by 2028. Hyundai Motor Group also plans to manufacture more than 300,000 actuator units annually at factories in the United States. Actuators are critical robot components that function as joints and muscles, reports Yohan News Agency (YNA). The deployment forms part of HMG’s phased strategy to integrate humanoid robots into core automotive manufacturing operations. While the group confirmed plans to deploy over 25,000 Atlas robots, it did not disclose a detailed rollout timeline or specify which plants will receive the robots first. During overseas road shows, Song Ho-sung, chief executive officer of Kia Corporation, said the humanoid robots are expected to begin operations at Hyundai Motor Group Metaplant America in Georgia in 2028. Deployment at Kia’s Georgia plant is planned for 2029 as part of the broader robotics expansion strategy, reports YNA. Recently, Boston Dynamics detailed the technology behind its Atlas humanoid robot’s ability to lift and carry heavy industrial objects using reinforcement learning and large-scale simulation training. In a new technical blog, the company demonstrated Atlas rotating its torso 180 degrees, squatting to pick up a mini-fridge, and transporting it while dynamically adjusting to shifting internal weight. The behavior was developed within weeks of Atlas’ public debut earlier this year. The system relies heavily on reinforcement learning, where Atlas repeatedly practices tasks in simulation environments under varying conditions. Engineers altered object weight, floor friction, grip force, and object placement to train the robot to adapt to unpredictable scenarios. Boston Dynamics said Atlas accumulated millions of simulated training hours running in parallel on GPUs. Training begins with a reference trajectory generated through animation or teleoperation. Atlas then receives rewards for maintaining balance, grip stability, and successful task completion while exposed to disturbances. Once reliable in simulation, the behavior is transferred to the physical robot for testing and refinement. Unlike many humanoids that depend mainly on vision systems, Atlas uses proprioception, or internal body awareness, to monitor balance, resistance, grip pressure, and body motion in real time. This allows the robot to handle unstable loads more effectively. Boston Dynamics said the new Atlas platform reduces the “sim-to-real gap” through simplified hardware architecture, symmetrical limbs, and only two actuator types, improving simulation accuracy and real-world performance. Jijo is an automotive and business journalist based in India. Armed with a BA in History (Honors) from St. Stephen's College, Delhi University, and a PG diploma in Journalism from the Indian Institute of Mass Communication, Delhi, he has worked for news agencies, national newspapers, and automotive magazines. In his spare time, he likes to go off-roading, engage in political discourse, travel, and teach languages. Premium Follow

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reports (2026) Cite this article 456 Accesses 1 Altmetric Metrics details We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. To address the challenges encountered by intelligent robots in perceiving high-dimensional environmental states and making adaptive trajectory planning decisions in complex topological environments, this paper presents a Graph Neural Network–Reinforcement Learning (GNN-RL) integrated framework, implemented based on the Soft Actor-Critic (SAC) algorithm for continuous control tasks. First, leveraging the topological modeling capability of GNNs, environmental entities are abstracted into graph nodes, and their spatial constraints and semantic associations are encoded as edge features. Through multi-layer graph convolution and adaptive edge weighting, high-dimensional structured environmental information is compressed into low-dimensional node-level and graph-level embeddings with rich topological semantics. This provides structured environmental cognition for the subsequent reinforcement learning module, alleviating the curse of dimensionality and enabling efficient action selection. Second, a dynamic collaborative mechanism between the GNN encoder and the SAC-based RL agent is established. The topological features extracted by the GNN are fed as input to the RL agent, which consists of twin Q-networks, a policy network, and a value network. A multi-objective reward function, which integrates safety, progress, and motion smoothness, guides the agent’s trial-and-error exploration. In this manner, static topological representations are transformed into dynamic trajectory policies, while the GNN parameters are jointly optimized end-to-end via the gradient signals from the RL loss function, overcoming the limitations of purely static graph learning. Finally, comprehensive comparative experiments are conducted in simulated complex topological environments, evaluating the proposed GNN-RL approach against DQN, PPO, and A* algorithms. The results show that the GNN-RL method achieves a favorable balance between perception accuracy and decision-making efficiency, providing a reliable and adaptive solution for robot navigation and trajectory planning in structured, dynamic environments. This work was funded by the Natural Science Foundation of Hunan Province of China (Grant No.2025JJ70191), Hunan Provincial Undergraduate Teaching Reform Research Project (Grant No.202401001576),Hunan Provincial Undergraduate Teaching Reform Key Research Project(Grant No.HNJG-2023B02), Scientific Research Project of Hunan Provincial Department of Education (Grant No. 25B0840),Scientific Research Project of Hunan Provincial Department of Education (Grant No. 24B0842). School of Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology, Hengyang, 421002, China Suo Zhang & Xuelin DU Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar Correspondence to Xuelin DU. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Zhang, S., DU, X. GNN enhanced reinforcement learning for robot navigation in complex topological networks. Sci Rep (2026). https://doi.org/10.1038/s41598-026-51938-5 Download citation Received: 16 March 2026 Accepted: 30 April 2026 Published: 20 May 2026 DOI: https://doi.org/10.1038/s41598-026-51938-5 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Advertisement Scientific Reports (Sci Rep) ISSN 2045-2322 (online) © 2026 Springer Nature Limited Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. UBTech taps Siemens’ digital manufacturing expertise to scale humanoid robot production to 10,000 units. Chinese robotics firm UBTech has signed a strategic cooperation agreement with Siemens Digital Industries Software to accelerate the large-scale manufacturing of humanoid robots and achieve an annual production capacity of 10,000 units in 2026. The agreement, signed in Shenzhen on March 16, focuses on integrating UBTech’s full-stack humanoid robotics capabilities with Siemens’ expertise in industrial digitalization and smart manufacturing. The partnership comes as demand for industrial humanoid robots continues to grow, pushing companies to move beyond prototypes toward scalable production. UBTech founder and CEO Zhou Jian said the company has seen a surge in orders this year, making mass production an urgent priority. “Mass production of tens of thousands of units has become a goal that we must achieve,” he said, adding that the collaboration with Siemens is a key step toward meeting that target. According to reports from Chinese automotive news platform Gasgoo and other industry outlets, Siemens will help UBTech design a comprehensive digital transformation roadmap covering the entire lifecycle, from research and development to full-scale production. The collaboration will rely heavily on Siemens’ industrial software portfolio, including tools for product design, simulation, process planning, and manufacturing management. These systems are expected to enable end-to-end digitization of UBTech’s operations, a critical requirement for scaling complex machines like humanoid robots. Humanoid robots combine precision engineering, AI systems, and motion control, making them significantly more complex to manufacture than conventional industrial robots. This complexity requires simulation-driven design, digital twins, and lifecycle management, areas where Siemens has decades of experience in global manufacturing. Shenzhen Daily notes that beyond technology integration, Siemens is also expected to provide technical training and support to help UBTech build the workforce capabilities needed for high-volume production. Globally, companies have demonstrated increasingly capable robots in recent years, but mass production remains a key bottleneck. UBTech has already begun moving in that direction. The company started delivering its Walker S2 industrial humanoid robots in 2025, marking an early step toward commercialization. At the same time, the company has reported strong market traction. In 2025, total orders for its humanoid robots exceeded 1.4 billion yuan, with applications spanning manufacturing and logistics, according to a Reuters report. Industry analysts say the next phase of competition will depend less on prototype performance and more on cost control, production efficiency, and real-world deployment at scale. For UBTech, achieving the 10,000-unit target would require not only higher production capacity but also improvements in supply chains, quality control, and system reliability, areas the Siemens partnership aims to address. Siemens’ Xcelerator platform and industrial software stack are designed to connect design, production, and operations into a unified digital workflow, potentially reducing time-to-market and improving consistency at scale. For UBTech, which has spent over a decade developing full-stack humanoid robot technologies, the partnership provides a pathway to translate technical capabilities into industrial output. More broadly, the deal signals increasing momentum in the humanoid robotics industry as companies race to prove that these machines can move from controlled demonstrations to reliable, scalable tools in factories. Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder. Premium Follow

ByJohn Koetsier, Senior Contributor. Will the home be the last place the humanoid robot gets a job? According to Sanctuary AI CEO James Wells, probably yes. That’s a bit of a gut punch for those of us who want our clothes laundered, dishes done, floors cleaned, houses tidied and maybe even our meals made by robots with Genesis AI or Kyber Labs hands. Certainly 1X with its Neo robot and Figure are very interested in the idea that humanoid robots will soon be at work in our homes. Wells, however, isn’t buying it. I caught up with James Wells at Web Summit Vancouver this week. Sanctuary is Canada’s only homegrown humanoid robotics company and holds what Wells says is the third-largest IP portfolio in the space globally. What Wells told me just might reframe much of the current humanoid hype cycle. 1X, which just kicked off full-scale production of its humanoid robot Neo, is one of the robot makers that have explicitly targeted the home market. In fact, I know someone who has pre-ordered Neo, which is targeted to ship before the end of this year. I asked Wells point-blank whether 1X's Neo, which is being pre-sold for home deployment now, is doomed. He didn’t bite the way I expected. "I applaud their marketing initiative," he said, choosing his words with care. "Which is a marketing initiative." Then he laid out Sanctuary’s internal ranking of deployment environments by viability: unit economics, environment complexity, customer sophistication, safety tolerance. By every axis, the home ranks last on Sanctuary AI’s list. Home gets there eventually, but Wells thinks humanoid robots are at least three to five years out for full commercial viability at performance and cycle times that customers will accept. I happen to know that some humanoid robotics companies are already testing their robots in homes, and there are definitely some current challenges. Breakage is one, as is fall risk, especially in households with small pets or babies. A concern is something like turning an oven on and forgetting to turn it off. “In the industrial world, you need to be 99.999% repeatable,” says Wells. “Most of these foundation models get you to about 80% performance. So you can do a lot of different things, but not that well. So you’re dropping a glass one out of five times.” This gap — between viral demo and reliable operation over time — is the real gap keeping humanoid robots from showing up at scale in our homes. That said: the rate of improvement is incredibly fast. Building great hands that work well and don’t break down is another key factor for success in the home as well as the factory. “Hands are the gating factor for physical AI to proliferate into the world,” Wells says. “The holy grail continues to be dexterous manipulation across a wide range of tasks, but folks have gone kind of ground up versus top down, meaning: let’s figure out the legs part and mobility, which … when you talk to all the customers out there, there’s not a lot of commercial utility there.” Translation: walking is sexy. The fully humanoid form factor is appealing. But it’s not where the value is for industry. That’s not an isolated viewpoint. It’s why Sonny, the humanoid-ish robot-in-training at Tutor Intelligence in Boston, has wheels, not legs. It’s more stable, more predictable, takes less energy, cheaper, simpler, longer-lasting and allows you to utilize more weight – including a battery – to make the entire platform more stable and to allow the robot more working time before a required recharge. Most experts I talk to say traditional robotic solutions, automation solutions and wheeled robotics are better options for factories and logistics facilities than humanoids. But, let’s be honest, wheels can be hard in homes. I’m testing a new home vacuum right now and – guess what – it can’t navigate stairs. (In fact, despite being “multi-floor capable,” it gets confused when we carry it upstairs.) Although they’re not the only options for ascending and descending stairs, legs are one way to open up our entire homes for robots. Hands, on the other hand, are what Sanctuary has specialized in since 2018. Unlike most modern robot hands, Sanctuary AI’s are hydraulic: a super-contrarian bet. While almost the entire rest of the industry is going tendon-driven, electric-motor-actuated for hands, Sanctuary AI miniaturized hydraulic valves. They’re coin-sized, food-safe-oil-actuated and tested past two billion cycles without degradation. The company says they are 50x faster and 6x cheaper than off-the-shelf components and offer higher power density than electric motors. “We have a unique capability with hydraulic hands that no one else in the world is doing, that has superior cycle life, speed, strength, robustness,” Wells says. “But our AI control system can control our humanoid, other humanoids, but also off the shelf hardware.” All of which brought up another point: at some level, humanoid robots aren’t products. They are labor, and that means they are GDP. I’m currently tracking pretty much every company, robot, investor, and funding round in the humanoid robot space, and the geographic concentration is stark: China leads, the U.S. is second, Japan, Germany, Korea and the UK are in the race, but both South America and Africa are not even in the game. Wells thinks robotics domination – both traditional and humanoid – is how China sees a path to push its share of global manufacturing from roughly 60% to 80%. In fact, he recently talked with Canada’s first-ever minister of AI about it: “If you do nothing, you will be forced to buy Chinese robots with AI brains that Canadian business will hire and you will hollow out the entire economy." The same is true for the United States and pretty much any other modern industrial society. The big question, whether for industry or home, is when we’ll see the iPhone moment in humanoid robots. Or, the ChatGPT moment: the point in time at which it becomes incredibly obvious that a massive phase shift in technology and capability is happening right now. There may not just be one, Wells says: "There’s going to be moments along the way," he said. "Task by task. Unlock a group of tasks, unlock another group of tasks." The endgame is what researchers call zero-shot learning: a robot walks into a brand-new situation and immediately starts doing useful work. The bare facts are that we’re not there yet, and it’s unclear when we will be. The reality is also, however, that we’re moving forward quickly.

January 02, 2023 09:00 ET | Source: STMicroelectronics N.V. STMicroelectronics N.V. STMicroelectronics and eYs3D Microelectronics to showcase collaboration on high-quality 3D stereo-vision camera for machine vision and roboticsat CES 2023 Geneva, Switzerland, and Taipei, Taiwan, January 2, 2023 – STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, and eYs3D Microelectronics, a fabless semiconductor design house that focuses on end-to-end hardware and software systems for computer vision, including advanced vision-processing System-on-Chip (SoC) devices, will reveal the results of their collaboration on high-quality machine vision at CES 2023 in Las Vegas on January 5-8. Using live demonstrations, the companies will show how stereo video and depth camera made from advanced active-coded infrared technology can enhance capabilities like feature recognition and autonomous guidance at mid-to-long working range. “STMicroelectronics’ advanced image sensors, using proprietary process technologies, offer class-leading pixel size while offering both high sensitivity and low crosstalk,” said James Wang, Chief Strategy & Sales Officer, eYs3D Microelectronics. “Such high-performance image sensors at a competitive price point enable us to achieve extremely compact system size while ensuring outstanding machine-vision performance. The strong connection we have established with ST increases our confidence to develop new products that will lead the machine vision market.” “The collaboration with eYs3D Microelectronics, through their expertise in capture, perception understanding, and 3D-fusion, offers ST additional business opportunities, use cases, and ecosystems addressing demands for stereo vision in applications such as robots, home-automation, home appliances, and many others,” said David Maucotel, Business Line Manager at ST’s Imaging Sub-Group. “While the reference designs showcased at CES are using monochromatic sensors, we can already foresee exciting enhancements and further use-cases using the RGB and RGB-IR versions of our sensors.”The CES demonstrations highlight two jointly developed reference designs, the Ref-B6 and Ref-B3 ASV (Active Stereo Vision) video and depth cameras. Both combine the eYs3D CV processor and eSP876 stereo 3D Depth-Map chipset with ST’s global shutter image sensors that provide enhanced near-infrared (NIR) sensitivity. The embedded eYs3D chipset enhances object edge detection, optimizes depth de-noising, and outputs HD-quality 3D depth data up to 60 fps frame rate. ST’s image sensors enable the cameras to output data streams in various combinations of video/depth resolution and frame rate for the best quality depth sensing and point-cloud creation. In addition, optimized lenses, filters and a VCSEL active-IR projector source optimize the infrared optical path and maximize immunity to ambient light noise. A specially developed control algorithm turns the IR projector on and off alternately to permit capturing artifact-free gray scale images. Leveraging this advanced hardware design, the Ref-B6 stereo-video camera achieves a 6-centimeter baseline and 85deg(H) x 70deg(V) depth field of view.Both eYs3D reference designs include the SDK (Software Development Kit) supporting Windows®, Linux and Android OS environments with multiple different programming languages and wrapper APIs. eYs3D will showcase the joint development Ref-B6 Depth Camera at two booth locations: LVCC, Booth #15769, Central Hall and Venetian, Eureka Park, Booth #62500, AT1, Hall G. Please contact your local sale representatives or sales@eys3d.com to arrange appointments and customer presentations. Note to Editors: Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. A point cloud is a discrete set of data points in space. The points may represent a 3D shape or object. About STMicroelectronicsAt ST, we are 48,000 creators and makers of semiconductor technologies mastering the semiconductor supply chain with state-of-the-art manufacturing facilities. An integrated device manufacturer, we work with more than 200,000 customers and thousands of partners to design and build products, solutions, and ecosystems that address their challenges and opportunities, and the need to support a more sustainable world. Our technologies enable smarter mobility, more efficient power and energy management, and the wide-scale deployment of the Internet of Things and connectivity. ST is committed to becoming carbon neutral by 2027. Further information can be found at www.st.com. Further information can be found at www.st.com. About eYs3D Microelectronics eYs3D Microelectronics Corp. pioneers in 3D sensing technologies, and aims to develop semiconductor oriented technologies and products related to 3D vision-simulating computer vision technologies integrated with computer intelligence. With its strong foundations and experiences in memory design and computer vision, as well as close co-operations with its parent company- Etron Technology, Inc., and ARM Holdings Plc. The company focuses on computer vision processors and specializes in 3D stereo vision solutions. As one of the earliest ventures in 3D technology, eYs3D was designed-in with multiple tier-one brands in VR, robotics and IoT devices. eYs3D’s state-of-the-art stereo vision depth IC and module offer customers more integrated value in bringing 3d sensing into real applications, realizing computer vision with human perception incorporated with A.I. For further information visit www.eys3d.com Press contacts: eYs3DOlivia WuCorporate Media RelationsEmail: marketing@eys3d.comSTMicroelectronicsMichael MarkowitzDirector Technical Media RelationsTel: +1 781 591 0354Email: michael.markowitz@st.com Attachments STMicroelectronics brings always-on vision to next-generation personal electronics with new ultralow-power image sensors VD55G4 and VD65G4 extend the ST BrightSense portfolio with compact,... STMicroelectronics apporte la vision « toujours active » à l’électronique personnelle de nouvelle génération avec de nouveaux capteurs d’image à très faible consommation ...

By Clinton Key Takeaways Rhoda AI closes $450 million Series A financing led by Premji Invest. Platform enables robots to operate autonomously in real-world, high-variability environments. Funding will scale AI models trained on internet videos for industrial robotics. Rhoda AI just raised $450 million in a round led by Premji Invest, pushing the company’s value up to $1.7 billion. Big names like Khosla Ventures, Temasek Holdings, and John Doerr also joined in. What sets Rhoda apart? The company trains its AI models on millions of internet videos, giving robots the know-how to tackle jobs in real factories and warehouses, not just in perfectly controlled labs. Jagdeep Singh, who used to run QuantumScape, started Rhoda to close the gap between digital AI and real-world robotics. Most old-school robots depend on teleoperation—basically, people steering robots from afar. That limits how much data they can learn from, and honestly, it leaves them struggling when things get unpredictable. Rhoda does things differently. Their method, Direct Video Action (DVA), first pre-trains AI models using huge online video libraries. Then, they fine-tune the system with actual robot data. This combo helps their robots handle all kinds of objects, positions, and even mistakes. The machines keep learning—watching, predicting, acting, and then checking their results—over and over, so they get smarter each time. Rhoda launched publicly after 18 months in stealth, unveiling FutureVision. This intelligence layer enables robots to operate autonomously across industrial workflows. Unlike conventional AI, FutureVision adapts dynamically to shifting layouts, unseen objects, and unpredictable conditions. In production trials, Rhoda completed complex component-processing workflows in under two minutes per cycle without human intervention. The platform demonstrated reliability in high-variability settings, surpassing key performance targets set by industrial partners. CEO Jagdeep Singh emphasised that the model’s advantage lies in learning from real-world video, not just pre-programmed sequences. “We aim to build robots that work in everyday environments, not just labs,” Singh said. By training on diverse internet data, the platform captures edge cases and generalises efficiently. Rhoda AI secures $450 million to expand autonomous industrial robots, research generative AI, and deploy Physical AI at scale. Source: Created by Ventureburn The $450 million Series A will support scaling industrial deployments, piloting with partners, and growing Rhoda’s team. The company plans to invest in research across generative AI, computer vision, and robotics engineering. Sandesh Patnam, Managing Partner at Premji Invest, noted that early deployment creates a “data flywheel” for continuous improvement. The funding enables Rhoda to capture real-world edge cases and refine robot behaviour at scale. The company also plans to license its AI model across various hardware and software platforms. Rhoda is positioned in the emerging Physical AI sector. Physical AI allows machines to perceive, reason, and act in real time. Beyond robotics, the field includes autonomous vehicles, adaptive assembly lines, robotic surgery, and smart buildings. Rhoda’s platform exemplifies systems that can think and act in unstructured environments. More News: Rebar Raises $14M to Expand AI Platform for HVAC and Construction Workflows Rhoda’s proprietary DVA model integrates perception and control, providing continuous feedback and physics-aware execution. FutureVision serves as the foundation for autonomous operations and is expected to power multiple industrial deployments. The company plans to eventually produce humanoid-style robots and develop in-house hardware to ensure quality for real-world tasks. By combining internet-scale pretraining with real-world adaptation, Rhoda allows robots to complete complex industrial tasks with minimal supervision. Rhoda AI has pulled in big-name investors like Capricorn Investment Group, Khosla Ventures, Leitmotif, Matter Venture Partners, Mayfield, Premji Invest, Prelude Ventures, Temasek, and Xora. The team brings together top talent in robotics, computer vision, and generative AI—these are folks who’ve worked at leading research labs and tech giants. Right now, Rhoda AI stands ready to push industrial robotics into its next chapter. By combining large-scale video pretraining with closed-loop predictive control, the company enables autonomous, adaptive, and scalable robot operations. To stay updated on crypto venture capital funding and market trends, visit our venture capital news section for more insights. Clinton Clinton Nwachukwu is a crypto and finance writer with an MBA in Artificial Intelligence and 6+ years of experience creating content for leading global brands. He turns complex topics into clear, actionable insights for readers worldwide. Disclaimer VentureBurn is a media platform covering the latest in cryptocurrency, artificial intelligence, venture capital, and the startup ecosystem. Opinions expressed on VentureBurn are for informational purposes only and do not constitute investment advice. Before making any high-risk investments in digital assets or emerging technologies, readers should conduct their own due diligence. All transactions and financial decisions are made at your own risk, and any losses incurred are solely your responsibility. VentureBurn does not endorse or recommend the buying or selling of any digital assets and is not a licensed investment advisor. Please note that VentureBurn may participate in affiliate marketing programs. 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Claire chatted to Melissa Greeff from Queen’s University about autonomous navigation and learning for drones. Melissa Greeff is an Assistant Professor in Electrical and Computer Engineering at Queen’s University. She leads Robora Lab and is also an Ingenuity Labs Robotics and AI Institute member. Her research interests include aerial robots, vision-based navigation, and safe learning-based control. Melissa’s expertise is in building autonomous aerial systems including previous experience in conducting field trials at various locations across Canada. She was listed as one of 50 women in robotics you need to know about in 2023 by the Women in Robotics organization.

Anbieter zum Thema Vom Automobilhersteller zum Robotik-Konzern? Was bei Tesla lange wie eine Vision von CEO Elon Musk klang, nimmt inzwischen konkrete Formen an. Steht hier die nächste große Plattform vor dem Durchbruch? Der globale Markt für humanoide Roboter steckt noch in den Anfängen, doch die Wachstumserwartungen sind außergewöhnlich. Analysten von Goldman Sachs schätzen, dass der Markt bis 2035 ein Volumen von 150 bis 200 Milliarden US-Dollar erreichen könnte, bei jährlichen Wachstumsraten von über 40 Prozent. Bis 2030 könnten weltweit über eine Million humanoide Roboter im Einsatz sein – zunächst in Industrie, Logistik und Pflege, später auch im Dienstleistungssektor. Zum Vergleich: Der heutige Markt für Industrieroboter umfasst rund 16 Milliarden US-Dollar jährlich. Humanoide Roboter würden diese Kategorie strukturell erweitern. Nicht ersetzen, sondern ergänzen. Im Zentrum der Aufmerksamkeit steht Tesla. Mit dem humanoiden Roboter Optimus verfolgt der Konzern einen radikal anderen Ansatz als klassische Robotik-Hersteller. Während Wettbewerber auf spezialisierte Aufgaben setzen, will Tesla eine universell einsetzbare Arbeitskraft entwickeln. Optimus ist rund 1,73 Meter groß, wiegt etwa 70 Kilogramm, kann laut Tesla bis zu 20 Kilogramm tragen und wird von denselben KI-Systemen gesteuert wie Teslas Fahrzeuge. Melden Sie sich an oder registrieren Sie sich und lesen Sie weiter Um diesen Artikel vollständig lesen zu können, müssen Sie registriert sein. Die kostenlose Registrierung bietet Ihnen Zugang zu exklusiven Fachinformationen. Kostenlosen Account erstellen Sie haben bereits ein Konto? Hier einloggen Melden Sie sich an oder registrieren Sie sich und lesen Sie weiter Um diesen Artikel vollständig lesen zu können, müssen Sie registriert sein. Die kostenlose Registrierung bietet Ihnen Zugang zu exklusiven Fachinformationen. Sie haben bereits ein Konto? Hier einloggen Weiterführende Inhalte Humanoide Robotik Partystimmung? Hört auf zu tanzen, liebe Roboter! Doppelt interessant! Elon Musk kombiniert Spacex mit XAI und Gegner Waymo rüstet auf Impressum Cookie-Manager Datenschutz Barrierefreiheit AGB Hilfe Mediadaten KI-Leitlinien Autoren Copyright © 2026 Vogel Communications Group Diese Webseite ist eine Marke von Vogel Communications Group. Eine Übersicht von allen Produkten und Leistungen finden Sie unter www.vogel.de

From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Researchers built soft robots that sense touch and movement without using cameras or external tracking. Researchers at the National University of Singapore have developed a soft robot system that gives machines a human-like sense of body awareness, allowing them to detect touch, external forces, and movement without relying on cameras or external tracking systems. The research focuses on proprioception, often called the body’s “sixth sense,” which helps humans understand body position and movement without looking. The team recreated a similar capability in soft robots using what they call an “expected perception” framework. The system allows the robot to predict how its body should move and compare that prediction with real-time sensory feedback. Any mismatch signals external contact or environmental interaction. Researchers say this helps the robot distinguish between its own movement and outside forces, something that has long challenged soft robotics. To test the technology, the team equipped a flexible robot with liquid-metal-based sensors capable of measuring bending, strain, and deformation. The robot then used internal sensing to navigate and react to physical interactions in real time. “Soft robots, too, need proprioception,” said Professor Cecilia Laschi from the Department of Mechanical Engineering at the National University of Singapore. According to the researchers, traditional soft robots struggle because their strain sensors react both to their own movement and to outside contact, making it difficult to determine what is actually happening around them. The new framework addresses that issue by mimicking how the human brain predicts sensory feedback. The robot calculates its expected body position based on movement commands and compares it with sensor readings gathered from its flexible structure. Researchers tested the system in a maze-navigation experiment where the robot moved autonomously without cameras. Instead, it relied entirely on touch and internal sensing to detect walls and adjust its movement path. In another test, a human operator guided the robot through movements similar to a massage or medical procedure performed on a manikin. The robot then learned and repeated those movements with high accuracy. “It could detect external contact within 0.4 seconds and distinguish its source with remarkable precision,” said Prof Laschi. “The robot also identified the direction of applied forces with an error margin below 10 degrees, even in dynamic environments.” The researchers believe the technology could improve human-robot interaction in healthcare, rehabilitation, and assistive robotics. Soft robots equipped with advanced sensing could eventually help elderly users, assist caregivers, or support surgeons during minimally invasive procedures. The team also sees applications in underwater robotics. Robots inspired by octopus arms, for example, could use touch-based perception to navigate environments where cameras may struggle due to darkness or poor visibility. “Robotics is inherently a cross-disciplinary field,” added Prof Laschi, pointing to the growing role of neuroscience, material science, artificial intelligence, and biology in shaping future robotic systems. Going forward, the researchers plan to improve the prediction system using machine learning models inspired by how human brains build internal representations from experience. The findings were published in Nature Communications. With over a decade-long career in journalism, Neetika Walter has worked with The Economic Times, ANI, and Hindustan Times, covering politics, business, technology, and the clean energy sector. Passionate about contemporary culture, books, poetry, and storytelling, she brings depth and insight to her writing. When she isn’t chasing stories, she’s likely lost in a book or enjoying the company of her dogs. Premium Follow

AI SEO that plans, writes & ranks - 90+ hours/month saved Personalized LinkedIn ads in minutes, not weeks. 40% higher B2B conversions. PageSpeed LLM is quietly reshaping which websites appear inside AI answers, summaries, and agents. You can have the best-written content in your niche, yet a slower stack or poorly tuned hosting layer can make LLMs reach for a competitor’s URL instead of yours. As answer engines factor in latency, freshness, and crawl cost, performance becomes a selection signal, not just a UX nicety. Understanding how these systems weigh speed, structure, and geography is now critical for any team that cares about organic visibility in AI surfaces. Large language models no longer rely solely on static training data scraped months ago. Many now blend pre-training with real-time crawling, search integration, and custom retrieval pipelines, which means your infrastructure and hosting choices can directly affect whether your pages are fetched, parsed, cached, and ultimately cited. This article connects web performance engineering, Core Web Vitals, and geolocation to LLM content selection, enabling marketing, SEO, and platform teams to make informed, measurable decisions. Advance Your SEO TABLE OF CONTENTS:How LLMs Select Web Content Behind the ScenesFour LLM retrieval modes to keep in mindPageSpeed LLM Content Selection DynamicsPageSpeed LLM mechanics inside AI crawlersGeolocation, Hosting, and Which Pages LLMs SurfaceRegional latency and AI answer variationsLocal and “near me” performance scenarios for teamsPerformance-First, LLM-Friendly Architecture and ContentRendering strategies for LLM crawlingPageSpeed LLM optimization checklist for dev and SEO teamsTesting How Performance Changes Your LLM VisibilityEstablishing your baseline: Performance plus AI presenceRunning controlled performance experimentsMonitoring, Tools, and Team WorkflowsDashboards that connect CWVs, logs, and AI citationsWhat web performance and SEO teams should ownTurning PageSpeed LLM Insights Into a Competitive AdvantageRelated Video How LLMs Select Web Content Behind the Scenes Para aquellos interesados en estrategias de marketing específicas, explorando ideas publicitarias de joyería puede ofrecer perspectivas valiosas sobre cómo impulsar las ventas en este sector. Before tuning performance, it helps to understand the fundamental ways LLMs touch the web. Each retrieval mode creates slightly different incentives around PageSpeed, structure, and availability. If you know which modes matter most for your audience, you can prioritize the right optimizations. Four LLM retrieval modes to keep in mind Modern models typically rely on a mix of offline and online data access. These are the four high-level patterns that matter for web performance planning. Pre-training and bulk crawling. Models are initially trained on large snapshots of the web obtained through bulk crawling. Here, crawl depth and frequency are influenced by how easy your site is to fetch and render; brittle, slow pages are less likely to be fully captured. Real-time browsing and “visit URL” tools. Some assistants can browse the live web to answer a specific prompt. When a user asks for a fresh comparison or a recent change, the model or its helper agent will follow links, obey robots directives, and respect timeouts. High latency or failed loads reduce the chance that your content is used in that answer. API connectors and integrations. LLM ecosystems are increasingly integrating with SaaS tools, documentation portals, and knowledge bases via APIs or search connectors. In these cases, endpoint response time and payload size strongly affect whether your content is considered usable inside time-constrained interactions. Retrieval-augmented generation (RAG). Many enterprise uses of LLMs rely on a RAG layer: vectors or keyword indices built from your content, paired with a retrieval service that feeds passages to the model. Retrieval performance, index freshness, and embedding latency all shape which pieces are surfaced when users ask questions. When teams design RAG systems on their own sites or documentation, they often discover how sensitive answer quality is to retrieval speed; the same sensitivity applies when external LLMs decide which public URLs to sample. Approaches that focus on LLM retrieval optimization for reliable RAG systems illustrate the same principle: the faster and cleaner your content is to access, the more consistently it is selected. PageSpeed LLM Content Selection Dynamics From the model’s perspective, every web request has a cost: time, tokens, and compute. Slow pages stretch latency budgets, increase timeout risk, and reduce the number of sources that can be consulted for a single answer. That cost pressure is why PageSpeed quietly shapes which URLs are preferred when multiple candidates could satisfy the same intent. Unlike traditional search ranking, where relevance and authority are discussed constantly, and performance is often treated as a secondary factor, LLM-driven systems must manage real-time interaction constraints. If an answer engine has a few seconds to respond, it may favor sources that consistently return usable HTML quickly over equally relevant sources that sometimes stall or require heavy client-side rendering. PageSpeed LLM mechanics inside AI crawlers Several familiar web performance metrics map cleanly to LLM behavior. While there is no public, universal threshold for any given platform, understanding what these metrics represent helps you reason about selection bias toward faster pages. Metric What it measures Likely impact on LLM retrieval Time to First Byte (TTFB) Server and network latency before the first byte of the response arrives High TTFB makes it harder for crawlers and browsing tools to stay within time budgets, so they may reduce crawl depth or abandon some requests. Largest Contentful Paint (LCP) How quickly the main content becomes visible to users When main text is delayed, bots that render pages may extract incomplete content or decide the page is not worth repeated visits. Interaction to Next Paint (INP) Responsiveness to user interactions Interactive tools built on LLMs, like agents or in-app browsers, may struggle with sluggish scripts and UI, leading to fewer interactions with your page. Cumulative Layout Shift (CLS) Visual stability as content loads Unstable layouts can make it harder for parsers to reliably locate headings, tables, and key text nodes when capturing your content. The key is that models and their surrounding systems often rely on machine-driven parsing and automated browsers. Clean, fast HTML with minimal blocking scripts makes their job easier, which increases the chance your page gets fully captured, cached, and reused across future answers. This is the performance layer of generative engine optimization and answer engine optimization, complementing traditional relevance and authority work. For organizations that know performance is a constraint but lack in-house expertise, partnering with specialists or reviewing an independent analysis of site speed optimization companies can accelerate the move from “good enough” to clearly superior latency in key regions. Advance Your SEO Geolocation, Hosting, and Which Pages LLMs Surface LLMs respond from data centers, but their upstream fetches still have to cross physical networks. Regional latency, CDNs, and data residency rules all shape which content is easiest for a user in a specific country to access. That means hosting architecture decisions can directly influence which domains and URLs appear in AI answers for different geographies. Regional latency and AI answer variations When users in different countries ask the same question, answer engines often have multiple viable sources. A documentation site hosted close to the model’s region, backed by a well-configured CDN, will typically respond faster than a similar site on a single distant server. Even without explicit favoritism, this relative speed can lead to different sources being selected because they fall within strict latency envelopes. Data residency and blocking add another wrinkle. If certain domains are slow or partially blocked in a jurisdiction, LLMs serving that region may implicitly downweight or avoid relying on them, even if their content is strong. Architectures that deploy replicas across multiple regions, align with local compliance requirements, and keep TLS handshakes fast give models greater confidence that they can reliably reach your content. Local and “near me” performance scenarios for teams Local-intent prompts, like “best coffee roaster near me” or “IT support in Berlin,” are increasingly answered by LLMs with a mix of directory data, reviews, and first-party business sites. When several candidates share similar ratings and descriptions, fast, stable sites can be more attractive retrieval targets than slow ones that risk timeouts or broken layouts in automated browsers. This creates a subtle competition between local business sites, aggregators, and maps or review platforms. A lightweight, well-structured local site served via a regional CDN node may be selected over a bloated directory page if the latter regularly triggers long TTFB or heavy client-side rendering in that geography. Treating local performance as part of a GEO strategy, rather than focusing solely on NAP data and reviews, helps capture emerging AI “near me” opportunities. Teams like Single Grain approach GEO (generative engine optimization) as both a content and infrastructure problem, aligning hosting, CDNs, and localization with the intent clusters they want to win in AI answers rather than viewing them as isolated projects. Performance-First, LLM-Friendly Architecture and Content Traditional UX optimization often focuses on how human users perceive speed: perceived load time, interactivity, and aesthetics. For LLMs and AI crawlers, the priority is different: fast access to clean, semantically structured HTML with minimal execution requirements. Aligning your architecture with that goal lets models extract what they need without fighting your front-end stack. Rendering strategies for LLM crawling Client-side rendering can be a major obstacle for AI systems that rely on automated browsers or headless fetchers, which have limited patience for complex JavaScript. If core content only appears after multiple script bundles execute, there is a higher chance that crawlers capture partial or empty pages. Server-side rendering or static generation, by contrast, ensures that the main text and headings are immediately available in the initial HTML payload. Pre-rendering HTML snapshots for key URL groups, such as documentation, product pages, and high-intent blog posts, can provide a fast path for both LLMs and traditional crawlers, while lazy-loading only non-essential widgets and interactive extras. A clear heading hierarchy and lean DOM also help models map your content into their internal topic graphs, a process explored in depth when aligning site architecture to LLM knowledge models through an AI topic graph. On the content side, ensuring that primary information lives in HTML text rather than being embedded in images or rendered entirely on the client makes extraction more reliable. When critical specs, pricing, or feature lists are buried in scripts or dynamically injected markup, answer engines may only see fragments of what you intended. For sites with complex product detail layouts, the same principles used in optimizing product specs pages for LLM comprehension apply: keep essential facts structured, close to the top of the document, and easy to parse without executing heavy code. PageSpeed LLM optimization checklist for dev and SEO teams To make this concrete, web performance and SEO teams can align on a shared checklist that focuses on machine readability and speed simultaneously. Each item can be translated into engineering tickets and acceptance criteria. Serve core content in the initial HTML. Headings, introductory copy, and key tables should render server-side so models can extract them without waiting for JavaScript execution. Keep TTFB and HTML size lean. Use caching, efficient frameworks, and CDN edge nodes to reduce backend latency and avoid bloated responses filled with unnecessary markup. Minimize render-blocking scripts and CSS. Defer non-critical JavaScript, split bundles, and inline only the CSS required for above-the-fold content so both users and bots see meaningful text quickly. Use semantic HTML and logical headings. Structured tags like <h2>, <h3>, <table>, and <ul> help automated parsers understand document sections, entities, and relationships. Limit DOM complexity on high-value pages. Excessive nested elements or countless nodes can slow down rendering and increase the chance that parsers miss important regions. Create lightweight variants for cornerstone content. For pages that attract a lot of AI-driven traffic, consider trimmed versions focused on factual clarity and speed, while richer interactive experiences can live elsewhere. When retrofitting existing content libraries, it is often faster to prioritize and refactor than to rewrite everything. Techniques for optimizing legacy blog content for LLM retrieval without rewriting it can be combined with this checklist to focus effort on the URLs most likely to influence AI answers. Advance Your SEO Testing How Performance Changes Your LLM Visibility Because LLM behavior is not fully transparent, many teams assume it cannot be optimized or measured. In practice, you can treat LLM visibility as an output metric and run controlled experiments, just as you would with conversion rates or search rankings. The key is to synchronize performance improvements with systematic observation of how often your pages are cited or surfaced. Establishing your baseline: Performance plus AI presence Start by assembling a baseline view of both technical and AI-facing signals. On the performance side, combine lab tools with real-user monitoring to understand TTFB, LCP, and other Core Web Vitals across your key regions. On the AI side, log which of your URLs appear in answers for a defined set of prompts relevant to your business, whether through manual testing or specialized tools. Some teams centralize this view by pairing their observability stack with dedicated LLM tracking software for brand visibility, which records when and where their content is cited across different models. Once this baseline exists, you can correlate it with performance changes over time rather than relying on anecdotes. Running controlled performance experiments With a baseline in place, treat PageSpeed improvements as experiments, not just refactors. This lets you answer questions like “Which optimizations actually increased our inclusion in AI answers?” instead of assuming all changes are equally valuable. Select a focused URL set. Choose groups of pages that target similar intents and currently appear occasionally or not at all in LLM answers, so shifts are easier to attribute. Define explicit performance goals. For each group, specify the latency and Core Web Vitals ranges you aim to reach, such as significantly lower TTFB in specific regions or more consistent LCP under realistic network conditions. Implement targeted optimizations. Apply changes like edge caching, SSR enablement, asset compression, or HTML simplification to one group, while leaving a comparable control group unchanged. Re-run standardized prompts. At scheduled intervals, query major models with the same prompt set, recording which sources and URLs they use, and whether they show your domain more often than before. Analyze patterns over time. Compare the experimental and control groups, looking for meaningful increases in citations, URL mentions, or paraphrased usage of your content that align with the performance gains. Iterating this process will build a playbook of which infrastructure and front-end changes have the highest impact on LLM selection for your specific domain, rather than guessing based on generic best practices. Monitoring, Tools, and Team Workflows Optimizing for PageSpeed in LLM interactions is not a one-time project; it is an ongoing collaboration among SEO, content, SRE, and application engineering. To keep improvements sustainable, teams need shared visibility and clear ownership lines so that performance regressions do not quietly erode AI visibility over time. Dashboards that connect CWVs, logs, and AI citations A useful pattern is to build a combined dashboard that pulls from web performance monitoring, server logs, and LLM tracking. One panel can show Core Web Vitals distributions and backend latency by region; another can list detected AI user agents and their crawl patterns; a third can summarize which pages are being cited in different answer engines. When this view is in place, anomalies become easier to spot. A sudden drop in AI citations for a group of URLs, coupled with a spike in TTFB or error rates in a particular region, quickly points to infrastructure issues. Likewise, increases in LLM references after a deployment that improved SSR coverage give concrete feedback that the work was worthwhile. What web performance and SEO teams should own Web performance and platform teams are best positioned to own low-level metrics like TTFB, error budgets, and JavaScript execution time. SEO and content teams, meanwhile, can lead on mapping high-value intents, identifying which URLs matter most for LLM inclusion, and defining the prompt sets used to test visibility. Each group should have explicit responsibilities that feed into a shared roadmap. Content strategists can also help prioritize which sections of long-form assets should be surfaced most prominently in HTML, while engineers ensure that these sections load quickly and reliably. When teams coordinate in this way, every sprint that improves performance also contributes directly to generative engine optimization outcomes rather than being treated as a pure infrastructure cost. If your organization wants outside support to align these disciplines, Single Grain frequently helps growth-focused companies run combined Core Web Vitals and AI visibility audits, then turn the findings into a pragmatic backlog. You can explore a tailored engagement or get a free consultation to benchmark your current position. Turning PageSpeed LLM Insights Into a Competitive Advantage LLM-driven experiences are making web performance a strategic visibility lever, not just a usability concern. When you understand how PageSpeed LLM dynamics influence crawling, caching, and citation decisions, you can design hosting, architecture, and content workflows that make your site the easiest and most reliable choice for AI systems to use. The path forward is to treat performance, geolocation, and machine readability as a single optimization surface. That means combining fast, regionally aware infrastructure with server-rendered, semantically rich HTML and a disciplined testing program that connects technical changes to shifts in AI answer patterns. As mentioned earlier, each organization’s results will differ, but the teams that measure will be the ones that discover which levers actually move their LLM presence. If you want a partner that already lives at the intersection of web performance engineering and AI-era search, Single Grain helps brands integrate SEVO, GEO, and technical optimization into one coherent strategy. Visit https://singlegrain.com/ to request a free consultation and turn PageSpeed LLM alignment into a durable growth advantage before your competitors do. Advance Your SEO Related Video https://www.youtube.com/watch?v=2Ru4DdRsfDA While technical performance is crucial, marketing teams should also focus on community-building efforts. For instance, employing proven tactics for Facebook group engagement can significantly boost brand visibility and audience interaction, complementing your SEO strategy. Understanding these dynamics is crucial for businesses aiming to optimize their digital presence. For instance, exploring innovative AI marketing strategies can provide valuable insights into how leading brands adapt to evolving digital landscapes. Ultimately, gaining visibility within LLM-generated content is only half the battle; the other half is converting that traffic. By focusing on optimizing your high-conversion marketing funnel, you can ensure that the audience you attract is effectively guided towards becoming customers. Frequently Asked Questions How is optimizing for LLM visibility different from traditional SEO and Core Web Vitals work? Traditional SEO focuses on ranking in search results and improving human-perceived speed, while LLM visibility requires making your content extremely fast and machine-readable under strict latency limits. The technical overlap is large, but prioritization shifts toward clean HTML, predictable response times, and content that can be reliably extracted without running complex front-end code. What are the early warning signs that poor page speed is hurting our presence in AI answers? Watch for patterns like your content being cited in some models but not others, competitors appearing more often in generic summaries, or AI tools favoring third-party aggregators over your first-party pages. When these shifts correlate with known performance issues or slow regions, it’s a strong indicator that latency is affecting selection. How should smaller sites with limited engineering resources approach PageSpeed LLM optimization? Smaller teams can get meaningful gains by using fast-managed hosting, a well-configured CDN, and a lightweight theme or framework instead of trying to custom-tune everything. Starting with a narrow set of high-intent pages and ensuring they are lean, static or server-rendered, and aggressively cached can deliver outsized visibility benefits. Which tool stack is best for ongoing LLM-focused performance monitoring? Combine a synthetic testing tool for repeatable lab benchmarks, a real-user monitoring solution for regional performance insights, and a log-based system that can identify AI-related user agents. Layer on an LLM citation tracker or prompt-testing tool so you can correlate technical metrics with how often your domain shows up in AI responses. How can we prioritize which pages to optimize first for LLM selection? Start with pages that sit closest to revenue or lead generation: documentation that drives adoption, comparison pages, core product or service overviews, and authoritative explainer content in your niche. Cross-reference these with any URLs already occasionally cited by AI tools, then focus initial performance work on the overlap. Are there risks in over-optimizing for speed when targeting LLMs? Yes, stripping pages down too aggressively can remove helpful context, internal links, or supportive media that both users and models rely on for nuance. The goal is to separate essential, fast-loading factual content from optional extras, not to sacrifice content quality or clarity in pursuit of microseconds. What should we ask potential vendors or agencies about their ability to improve PageSpeed for LLM visibility? Ask how they measure success beyond generic Core Web Vitals, including how they plan to track changes in AI citations or answer inclusion for your domain. Request examples of projects where they improved both regional latency and machine readability, and clarify how they’ll coordinate with your SEO and content teams rather than treating this as a purely DevOps project. If you were unable to find the answer you’ve been looking for, do not hesitate to get in touch and ask us directly. Advance Your SEO Para aquellos interesados en estrategias de marketing específicas, explorando ideas publicitarias de joyería puede ofrecer perspectivas valiosas sobre cómo impulsar las ventas en este sector. Before tuning performance, it helps to understand the fundamental ways LLMs touch the web. Each retrieval mode creates slightly different incentives around PageSpeed, structure, and availability. If you know which modes matter most for your audience, you can prioritize the right optimizations. Modern models typically rely on a mix of offline and online data access. These are the four high-level patterns that matter for web performance planning. When teams design RAG systems on their own sites or documentation, they often discover how sensitive answer quality is to retrieval speed; the same sensitivity applies when external LLMs decide which public URLs to sample. Approaches that focus on LLM retrieval optimization for reliable RAG systems illustrate the same principle: the faster and cleaner your content is to access, the more consistently it is selected. From the model’s perspective, every web request has a cost: time, tokens, and compute. Slow pages stretch latency budgets, increase timeout risk, and reduce the number of sources that can be consulted for a single answer. That cost pressure is why PageSpeed quietly shapes which URLs are preferred when multiple candidates could satisfy the same intent. Unlike traditional search ranking, where relevance and authority are discussed constantly, and performance is often treated as a secondary factor, LLM-driven systems must manage real-time interaction constraints. If an answer engine has a few seconds to respond, it may favor sources that consistently return usable HTML quickly over equally relevant sources that sometimes stall or require heavy client-side rendering. Several familiar web performance metrics map cleanly to LLM behavior. While there is no public, universal threshold for any given platform, understanding what these metrics represent helps you reason about selection bias toward faster pages. The key is that models and their surrounding systems often rely on machine-driven parsing and automated browsers. Clean, fast HTML with minimal blocking scripts makes their job easier, which increases the chance your page gets fully captured, cached, and reused across future answers. This is the performance layer of generative engine optimization and answer engine optimization, complementing traditional relevance and authority work. For organizations that know performance is a constraint but lack in-house expertise, partnering with specialists or reviewing an independent analysis of site speed optimization companies can accelerate the move from “good enough” to clearly superior latency in key regions. Advance Your SEO Advance Your SEO LLMs respond from data centers, but their upstream fetches still have to cross physical networks. Regional latency, CDNs, and data residency rules all shape which content is easiest for a user in a specific country to access. That means hosting architecture decisions can directly influence which domains and URLs appear in AI answers for different geographies. When users in different countries ask the same question, answer engines often have multiple viable sources. A documentation site hosted close to the model’s region, backed by a well-configured CDN, will typically respond faster than a similar site on a single distant server. Even without explicit favoritism, this relative speed can lead to different sources being selected because they fall within strict latency envelopes. Data residency and blocking add another wrinkle. If certain domains are slow or partially blocked in a jurisdiction, LLMs serving that region may implicitly downweight or avoid relying on them, even if their content is strong. Architectures that deploy replicas across multiple regions, align with local compliance requirements, and keep TLS handshakes fast give models greater confidence that they can reliably reach your content. Local-intent prompts, like “best coffee roaster near me” or “IT support in Berlin,” are increasingly answered by LLMs with a mix of directory data, reviews, and first-party business sites. When several candidates share similar ratings and descriptions, fast, stable sites can be more attractive retrieval targets than slow ones that risk timeouts or broken layouts in automated browsers. This creates a subtle competition between local business sites, aggregators, and maps or review platforms. A lightweight, well-structured local site served via a regional CDN node may be selected over a bloated directory page if the latter regularly triggers long TTFB or heavy client-side rendering in that geography. Treating local performance as part of a GEO strategy, rather than focusing solely on NAP data and reviews, helps capture emerging AI “near me” opportunities. Teams like Single Grain approach GEO (generative engine optimization) as both a content and infrastructure problem, aligning hosting, CDNs, and localization with the intent clusters they want to win in AI answers rather than viewing them as isolated projects. Traditional UX optimization often focuses on how human users perceive speed: perceived load time, interactivity, and aesthetics. For LLMs and AI crawlers, the priority is different: fast access to clean, semantically structured HTML with minimal execution requirements. Aligning your architecture with that goal lets models extract what they need without fighting your front-end stack. Client-side rendering can be a major obstacle for AI systems that rely on automated browsers or headless fetchers, which have limited patience for complex JavaScript. If core content only appears after multiple script bundles execute, there is a higher chance that crawlers capture partial or empty pages. Server-side rendering or static generation, by contrast, ensures that the main text and headings are immediately available in the initial HTML payload. Pre-rendering HTML snapshots for key URL groups, such as documentation, product pages, and high-intent blog posts, can provide a fast path for both LLMs and traditional crawlers, while lazy-loading only non-essential widgets and interactive extras. A clear heading hierarchy and lean DOM also help models map your content into their internal topic graphs, a process explored in depth when aligning site architecture to LLM knowledge models through an AI topic graph. On the content side, ensuring that primary information lives in HTML text rather than being embedded in images or rendered entirely on the client makes extraction more reliable. When critical specs, pricing, or feature lists are buried in scripts or dynamically injected markup, answer engines may only see fragments of what you intended. For sites with complex product detail layouts, the same principles used in optimizing product specs pages for LLM comprehension apply: keep essential facts structured, close to the top of the document, and easy to parse without executing heavy code. To make this concrete, web performance and SEO teams can align on a shared checklist that focuses on machine readability and speed simultaneously. Each item can be translated into engineering tickets and acceptance criteria. When retrofitting existing content libraries, it is often faster to prioritize and refactor than to rewrite everything. Techniques for optimizing legacy blog content for LLM retrieval without rewriting it can be combined with this checklist to focus effort on the URLs most likely to influence AI answers. Advance Your SEO Advance Your SEO Because LLM behavior is not fully transparent, many teams assume it cannot be optimized or measured. In practice, you can treat LLM visibility as an output metric and run controlled experiments, just as you would with conversion rates or search rankings. The key is to synchronize performance improvements with systematic observation of how often your pages are cited or surfaced. Start by assembling a baseline view of both technical and AI-facing signals. On the performance side, combine lab tools with real-user monitoring to understand TTFB, LCP, and other Core Web Vitals across your key regions. On the AI side, log which of your URLs appear in answers for a defined set of prompts relevant to your business, whether through manual testing or specialized tools. Some teams centralize this view by pairing their observability stack with dedicated LLM tracking software for brand visibility, which records when and where their content is cited across different models. Once this baseline exists, you can correlate it with performance changes over time rather than relying on anecdotes. With a baseline in place, treat PageSpeed improvements as experiments, not just refactors. This lets you answer questions like “Which optimizations actually increased our inclusion in AI answers?” instead of assuming all changes are equally valuable. Iterating this process will build a playbook of which infrastructure and front-end changes have the highest impact on LLM selection for your specific domain, rather than guessing based on generic best practices. Optimizing for PageSpeed in LLM interactions is not a one-time project; it is an ongoing collaboration among SEO, content, SRE, and application engineering. To keep improvements sustainable, teams need shared visibility and clear ownership lines so that performance regressions do not quietly erode AI visibility over time. A useful pattern is to build a combined dashboard that pulls from web performance monitoring, server logs, and LLM tracking. One panel can show Core Web Vitals distributions and backend latency by region; another can list detected AI user agents and their crawl patterns; a third can summarize which pages are being cited in different answer engines. When this view is in place, anomalies become easier to spot. A sudden drop in AI citations for a group of URLs, coupled with a spike in TTFB or error rates in a particular region, quickly points to infrastructure issues. Likewise, increases in LLM references after a deployment that improved SSR coverage give concrete feedback that the work was worthwhile. Web performance and platform teams are best positioned to own low-level metrics like TTFB, error budgets, and JavaScript execution time. SEO and content teams, meanwhile, can lead on mapping high-value intents, identifying which URLs matter most for LLM inclusion, and defining the prompt sets used to test visibility. Each group should have explicit responsibilities that feed into a shared roadmap. Content strategists can also help prioritize which sections of long-form assets should be surfaced most prominently in HTML, while engineers ensure that these sections load quickly and reliably. When teams coordinate in this way, every sprint that improves performance also contributes directly to generative engine optimization outcomes rather than being treated as a pure infrastructure cost. If your organization wants outside support to align these disciplines, Single Grain frequently helps growth-focused companies run combined Core Web Vitals and AI visibility audits, then turn the findings into a pragmatic backlog. You can explore a tailored engagement or get a free consultation to benchmark your current position. LLM-driven experiences are making web performance a strategic visibility lever, not just a usability concern. When you understand how PageSpeed LLM dynamics influence crawling, caching, and citation decisions, you can design hosting, architecture, and content workflows that make your site the easiest and most reliable choice for AI systems to use. The path forward is to treat performance, geolocation, and machine readability as a single optimization surface. That means combining fast, regionally aware infrastructure with server-rendered, semantically rich HTML and a disciplined testing program that connects technical changes to shifts in AI answer patterns. As mentioned earlier, each organization’s results will differ, but the teams that measure will be the ones that discover which levers actually move their LLM presence. If you want a partner that already lives at the intersection of web performance engineering and AI-era search, Single Grain helps brands integrate SEVO, GEO, and technical optimization into one coherent strategy. Visit https://singlegrain.com/ to request a free consultation and turn PageSpeed LLM alignment into a durable growth advantage before your competitors do. Advance Your SEO Advance Your SEO https://www.youtube.com/watch?v=2Ru4DdRsfDA While technical performance is crucial, marketing teams should also focus on community-building efforts. For instance, employing proven tactics for Facebook group engagement can significantly boost brand visibility and audience interaction, complementing your SEO strategy. Understanding these dynamics is crucial for businesses aiming to optimize their digital presence. For instance, exploring innovative AI marketing strategies can provide valuable insights into how leading brands adapt to evolving digital landscapes. Ultimately, gaining visibility within LLM-generated content is only half the battle; the other half is converting that traffic. By focusing on optimizing your high-conversion marketing funnel, you can ensure that the audience you attract is effectively guided towards becoming customers. Traditional SEO focuses on ranking in search results and improving human-perceived speed, while LLM visibility requires making your content extremely fast and machine-readable under strict latency limits. The technical overlap is large, but prioritization shifts toward clean HTML, predictable response times, and content that can be reliably extracted without running complex front-end code. Watch for patterns like your content being cited in some models but not others, competitors appearing more often in generic summaries, or AI tools favoring third-party aggregators over your first-party pages. When these shifts correlate with known performance issues or slow regions, it’s a strong indicator that latency is affecting selection. Smaller teams can get meaningful gains by using fast-managed hosting, a well-configured CDN, and a lightweight theme or framework instead of trying to custom-tune everything. Starting with a narrow set of high-intent pages and ensuring they are lean, static or server-rendered, and aggressively cached can deliver outsized visibility benefits. Combine a synthetic testing tool for repeatable lab benchmarks, a real-user monitoring solution for regional performance insights, and a log-based system that can identify AI-related user agents. Layer on an LLM citation tracker or prompt-testing tool so you can correlate technical metrics with how often your domain shows up in AI responses. Start with pages that sit closest to revenue or lead generation: documentation that drives adoption, comparison pages, core product or service overviews, and authoritative explainer content in your niche. Cross-reference these with any URLs already occasionally cited by AI tools, then focus initial performance work on the overlap. Yes, stripping pages down too aggressively can remove helpful context, internal links, or supportive media that both users and models rely on for nuance. The goal is to separate essential, fast-loading factual content from optional extras, not to sacrifice content quality or clarity in pursuit of microseconds. Ask how they measure success beyond generic Core Web Vitals, including how they plan to track changes in AI citations or answer inclusion for your domain. Request examples of projects where they improved both regional latency and machine readability, and clarify how they’ll coordinate with your SEO and content teams rather than treating this as a purely DevOps project. Eric Siu is a seasoned entrepreneur and CEO of the digital marketing agency Single Grain, which drives scalable and predictable revenue growth using paid ads, SEO, and content marketing. He has successfully scaled multiple businesses and assisted clients in various industries, including Amazon, Uber, and Salesforce, to do the same. Eric hosts two podcasts: Marketing School with Neil Patel and Leveling Up, where he dissects growth levers that help businesses scale. Follow him on Twitter @ericosiu. Our newsletter is brimming with marketing strategies that are working right now and must-have resources. Join our community of 15,000+ subscribers, including professionals from Amazon, Google, and Samsung. Join 15,000+ marketers getting proven strategies Single Grain is a full-service digital marketing agency that helps great companies grow their revenues online. Get in touch: contact@singlegrain.com © 2026 Single Grain. All rights reserved. Sitemap | Privacy Policy | Personal Data Removal Request | Notice of Non-Affiliation | Accessibility Get Free Instant Access 8 Effective Online Marketing Tactics That Have Generated 1,545%+ ROI for our Customers (and You Can Easily Use) We hate SPAM and promise to keep your email address safe. Personal attention guaranteed You'll hear back from me or one of our senior strategists directly. "Single Grain was instrumental to our growth. They're especially ahead of the game with AI." — Yaniv Masjedi, Co-Founder & CMO, Nextiva Trusted by teams at Amazon, Uber, Salesforce, and Airbnb ClickFlow’s AI plans and writes production-grade content — so you don’t need 10 more writers and editors. Early adopters average 27% more organic traffic in 6 months. Karrot generates personalized ads and landing pages for every target account in minutes, not weeks. One team closed 2 deals from just 15 accounts in under 2 weeks.

Naver's executives are seen at the Global AI Summit hosted by the Saudi Data & AI Authority in Riyadh, Saudi Arabia, in this photo provided by the Korean company, Sept. 12. Yonhap Naver, the operator of Korea's largest internet platform, has signed an initial agreement with Saudi Arabia's artificial intelligence (AI) agency to jointly develop an Arabic language-based large language model (LLM), company officials said Friday. During the Global AI Summit hosted by the Saudi Data & AI Authority (SDAIA) in Saudi Arabia's capital of Riyadh earlier this week, Naver and the SDAIA signed the memorandum of understanding (MOU) to cooperate in various sectors, including AI, cloud computing, data centers and robots, according to the officials. Under the MOU, the two sides plan to jointly develop an Arabic LLM, and technology solutions and services in the fields. SDAIA has been leading the Middle Eastern nation's ambitious plan of creating a technology-driven economy by 2030. Last year, Naver also struck a deal with the Saudi Arabian government to create a digital twin platform for Riyadh and four other Saudi cities. (Yonhap)

i gaudeix dels avantatges de ser subscriptor El futur connectat Director de Motor de Prensa Ibérica Especialista en Periodista de motor centrat en el sector de l'automòbil i la motocicleta, així com en totes les árees d'economía relacionades amb la industria de l'automoció, la movilitat sostenible i l'electrifcació. Ubicada/t a Barcelona Alguns fins i tot tenen nom. Atlas, Mornine, Optimus, Iron, Asimo, Phoenix, Apollo..., tots semblen trets d’una pel·lícula de ciència-ficció. Blade Runner; Jo, robot, Transformers, i fins i tot Terminator, títols de cine d’èxit, els incorporarien en el seu repartiment. Però aquests són molt reals i venen sobre rodes. La irrupció de la intel·ligència artificial en el sector de l’automòbil (fa anys) ha derivat en la creació de dispositius de mobilitat pròpia, robots intel·ligents a priori controlats, amb un objectiu que ja va més enllà de demostrar que els fabricants de cotxes són bons en això de la tecnologia. Ja fa anys que les marques d’automoció mostren els seus avanços en robòtica. Honda i Toyota van ser de les primeres en mostrar la capacitat industrial creant humanoides i robots amb diferents usos. El 2000, Honda va presentar el seu projecte Asimo. Era un petit robot humanoide amb pinta d’astronauta que caminava, corria (a 9 km/h), pujava escales i interactuava amb humans. La seva última versió data del 2014 quan es va presentar a Europa amb els seus 1,3 metres d’altura i 50 quilos de pes. Reconeixia veus i sons, rostres i gestos, i rebia unes ordres simples. El 2008 va arribar a ser director per unes hores de l’Orquestra Simfònica de Detroit, en les albors de la IA aplicada a l’automoció. Apollo, el robot humanoide de Mercedes-Benz. / El violinista Entre el 2007 i el 2008, Toyota va mostrar al món els seus robots músics. El robot violinista. No li van posar nom, pobre. Era un humanoide d’1,5 metres d’altura dissenyat per Toyota Motor Corp, que va exhibir la seva destresa manual tocant el violí amb vibrato i moviments precisos. Tenia 17 articulacions i estava pensat per ser un assistent a la llar o en hospitals. Tot el treball de robòtica el va supervisar Gill Pratt, el CEO del Toyota Research Institute i exdirector d’un projecte de defensa en l’Agència de Projectes d’Investigació Avançada de Defensa dels EUA (DARPA). I és que Toyota té una llarga tradició en la robòtica aplicada al servei de les persones. I compta amb una divisió que desenvolupa pròtesis robòtiques amb finalitats mèdiques. Fa un parell d’anys, Zhang Guibing, màxim responsable de negoci internacional de l’automobilística xinesa Chery Group, va presentar el seu projecte de robòtica amb què la companyia pensa equipar els seus concessionaris del futur. El projecte desenvolupat per AiMoga (Ai per IA en anglès i Moga per Multi Objective Genetic Algorithm), ja té nom i parla: és Mornine (tot i que també respon al nom MoiMoi). Fa 1,65 metres i pesa 65 quilos. És l’únic robot amb IA de l’automoció amb aspecte humà. Fruit de la col·laboració de Chery amb Huawei, Nvidia i Horizon Robotics. La seva forma humanoide amaga un sofisticat sistema d’IA que intenta replicar al màxim la interacció social humana. Així, quan es parla amb Mornine, el robot no només interpreta les ordres de veu: també analitza el llenguatge del cos i l’entorn per respondre amb la màxima precisió gràcies al desenvolupament d’un cervell de doble nucli de DeepSeek. Té una autonomia de dues hores i una bateria de 0,7 KWh que es carrega en dues hores més. Es mou a una velocitat màxima d’un metre per segon i percep l’entorn que l’envolta gràcies a un conjunt de càmeres, sistema d’ultrasò i la tecnologia de detecció làser LiDAR. A més, està equipat amb IA tipus LLM, un model de llenguatge cognitiu (parla 10 idiomes) que li permet reconèixer patrons del llenguatge per generar una conversa tant natural com sigui possible. El seu àlter ego és a Tesla. És un model d’aspecte més agressiu. El seu nom: Optimus. Elon Musk ja el va mostrar de manera embrionària el 2021, tot i que va ser el 2025 quan el va posar a ballar. És un homenatge a Optimus Prime, el robot protagonista de Transformers. Optimus fa 1,73 metres i pesa 57 quilos. Pot caminar a 8 quilòmetres per hora i utilitza xarxes neuronals avançades per navegar. Té habilitats per manipular objectes delicats, cuinar, netejar i transportar càrregues, gràcies a mans de cinc dits i alta capacitat de càrrega. Funciona amb tecnologia de visió artificial i bateries de Tesla. L’objectiu és un robot bípede que millori la productivitat a les fàbriques des d’aquest any. Musk va anunciar que presentarà la versió Optimus 3, en la qual invertirà 20.000 milions. Hyundai Motor Group també es va apuntar, el 2025, a aquesta revolució. En el CES de Las Vegas, el fabricant coreà va presentar la seva estratègia de robòtica amb IA, que va més enllà dels vehicles i situa els robots com una eina estructural del seu futur industrial. El seu nom: Atlas. Hyundai també va mostrar robots creats amb els seus socis de Boston Dynamics: Spot i Stretch. Per a Hyundai no es tracta només de software o simulacions digitals, sinó de crear sistemes capaços de recopilar dades en fàbriques, centres logístics o instal·lacions industrials i prendre decisions de manera autònoma. S’implementarà el 2028. El 2024, BMW va firmar un acord amb la start-up de robòtica Figure per introduir els seus robots humanoides a la seva planta de Spartanburg, Carolina del Sud (EUA). Aquell any, Mercedes-Benz va firmar un acord similar amb Apptronik per experimentar amb el model Apollo, un robot bípede que pot aixecar fins a 25 kg. N’hi ha molts més, i d’altres que arribaran. L’últim en fer la seva aparició ha sigut Iron, l’humanoide presentat per la xinesa Xpeng. Ja ets subscriptor o usuari registrat? Inicia sessió Per disfrutar d’aquests continguts gratis has de navegar registrat. 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Publicado el 4 abril, 2026 Investigadores de Huawei Noah’s Ark Lab, la Universidad Técnica de Darmstadt y ETH Zürich han desarrollado ROS-LLM, un framework open source publicado en Nature Machine Intelligence que conecta modelos de lenguaje (LLMs) con el Robot Operating System (ROS, la plataforma estándar para control de robots) para que las máquinas puedan interpretar instrucciones en lenguaje natural y convertirlas en acciones físicas paso a paso. El sistema funciona así: un usuario da una orden como «coge el bloque verde y ponlo en la estantería negra». El LLM interpreta la instrucción, la descompone en acciones atómicas (acercarse, agarrar, mover, soltar) y las traduce a comandos ejecutables en ROS. Soporta tres modos de ejecución (código inline, árboles de comportamiento y máquinas de estados), puede aprender nuevas habilidades por imitación (un humano demuestra la tarea y el sistema la incorpora a su biblioteca de acciones), y mejora continuamente mediante reflexión con feedback humano y ambiental. Lo más relevante: no requiere que el usuario sepa programar. Un operador sin conocimientos técnicos puede dar instrucciones en lenguaje natural y el robot las ejecuta. El código es completamente open source, lo que significa que cualquier equipo de robótica puede integrarlo con su hardware existente. Mi valoración: la robótica lleva décadas atrapada en un cuello de botella: cada nueva tarea requiere que un ingeniero la programe manualmente. ROS-LLM no elimina ese cuello de botella (las acciones atómicas base sí las programan expertos), pero lo reduce drásticamente. Una vez que el robot tiene una biblioteca de habilidades básicas, un no-experto puede combinarlas con lenguaje natural para tareas que nadie anticipó. Que sea open source y compatible con ROS (el estándar de facto) significa que la adopción puede ser rápida. ¿Qué es ROS-LLM? Un framework open source que conecta modelos de lenguaje con ROS para que robots ejecuten órdenes en lenguaje natural. ¿Dónde se publicó? En Nature Machine Intelligence. Código open source disponible. ¿Necesito saber programar? No para operar el robot. Las acciones atómicas base las configuran expertos; después, cualquiera puede combinarlas con lenguaje natural. por Natalia Polo Análisis diario, herramientas y tutoriales sobre IA en wwwhatsnew.

Paul Miller , VP, Principal Analyst Writing about last year’s Hannover Messe, I made a point of calling out the small number of humanoid robots I saw at this huge industrial trade show. Fast-forward to 2026, and I’m about to jump on a plane to Germany for this year’s event. I know that I’m going to see a lot of humanoid robots. I know that most of them will be Chinese, a few will be European, and many of them will be impressive. And as Charlie Dai and I argue in our new report, I am confident that exhibitors’ and attendees’ apparent obsession with legs and arms misses the real story. Our new report, Physical AI Perceives, Reasons, And Acts In The Real World, argues that the more important story is really the growing capability of physical AI. Humanoid robots like those that my colleague Charlie Dai recently wrote about benefit from that, for sure, but so do many other types of physical automation — and most of them are cheaper, more durable, and more useful than the bundle of compromises required to squeeze batteries, computers, sensors, actuators, and more into a vaguely humanoid shape. I’ll be discussing the background to the report’s findings and answering questions in a client webinar on June 24: Sign up now to participate live or to receive the recording after we’re done. Physical AI is all about bringing AI into the real world, making AI aware of what’s happening around it, and giving AI the ability to affect — touch — that world. As we describe in the report, physical AI comprises four broad capabilities. Each is a huge field of fast-moving research in its own right, but something special happens when all four are brought together to deliver physical AI, which: I’ll be looking for evidence of physical AI throughout my week in Hannover, and I’ll blog about that (and other highlights of the show) once I’ve had time to digest everything I see. As always, if you have your own perspectives to share, please schedule a briefing and tell us all about them. If you’re a Forrester client and want to discuss (or challenge) our thinking on these topics, please schedule an inquiry or guidance session. Stay tuned for updates from the Forrester blogs. Stay tuned for updates from the Forrester blogs.

Mariano R. 23 avril 2026 3 minutes de lecture Business & Transformation Avec leur alliance, NEURA Robotics et Amazon Web Services veulent avancer dans l’industrialisation de l’IA physique. Ils vont connecter robotique avancée et infrastructure cloud mondiale. L’objectif est de déployer des millions de robots cognitifs d’ici 2030. En s’associant, NEURA Robotics et AWS s’attaquent à l’un des défis les plus structurants du secteur. C’est celui de transformer des systèmes intelligents encore limités en solutions robustes qui opèrent à grande échelle dans des environnements industriels. Il sont convaincu que sans données réelles, sans puissance de calcul distribuée et sans validation terrain, l’IA physique restera cantonnée à des démonstrateurs. Ce partenariat va donc lever ces freins, et poser les bases d’une infrastructure globale. Les robots, les données et l’apprentissage continu fonctionneront de manière intégrée. Le partenariat s’articule autour de trois piliers complémentaires. D’abord, l’infrastructure. AWS hébergera le Neuraverse. Il s’agit de l’environnement numérique de NEURA qui centralise l’entraînement et le partage des données robotiques à grande échelle. Ensuite, le développement de l’IA. Les environnements NEURA Gym (des installations où les robots s’entraînent à des tâches complexes en simulation et conditions contrôlées ) seront connectés à Amazon SageMaker. Cette intégration va faire avancer la formation des modèles. Elle va aussi standardiser les processus d’apprentissage pour les partenaires industriels. Enfin, la validation terrain. Amazon explore déjà l’intégration des robots NEURA dans certains centres logistiques. Un terrain d’expérimentation stratégique, où les cas d’usage réels permettront d’affiner rapidement les capacités des robots. Par exemple la manutention, le tri, ou la collaboration homme-machine. Les modèles de langage sont nourris par des milliards de données issues d’Internet. Mais les robots, eux, souffrent d’un déficit structurel de données d’entraînement. Or, pour évoluer dans des contextes physiques imprévisibles, l’apprentissage doit s’appuyer sur des expériences concrètes. C’est précisément ce point que l’alliance cherche à adresser. NEURA apporte sa plateforme de robotique cognitive et sa couche d’intelligence, pour permettre aux machines de s’adapter en temps réel. De son côté, AWS met à disposition une infrastructure cloud mondiale. Celle qui collecte, traite et partage des volumes massifs de données entre flottes de robots. Ils souhaitent créer des boucles d’apprentissage continues entre simulation et réalité, afin d’accélérer la progression des systèmes. 🤖 @NEURARobotics and AWS announced a strategic agreement to accelerate Physical AI at scale, combining NEURA's cognitive robotics platform with AWS's cloud and AI infrastructure to help train, validate, and deploy the next generation of intelligent robots. 🔗… pic.twitter.com/oNt7PKDOwc Ce rapprochement répond à une problématique bien identifiée dans l’industrie. Celui de passer du prototype à la production. Déployer des robots intelligents ne se limite pas à concevoir du matériel performant. Cela exige une infrastructure robuste qui supporte l’apprentissage continu, la mise à jour des modèles et la gestion de flottes à grande échelle. La capacité de calcul d’AWS, sa couverture mondiale et son portefeuille de services d’IA en font un levier d’industrialisation. Pour NEURA, exécuter le Neuraverse sur cette infrastructure permet de raccourcir les cycles de développement. Cela va également rendre les performances reproductibles, quel que soit le contexte d’utilisation. Ce partenariat illustre aussi la convergence entre innovation robotique européenne et infrastructures cloud dominées par des acteurs américains. NEURA apporte une expertise pointue en robotique cognitive. Avec une approche intégrée où matériel et intelligence sont conçus conjointement. AWS, lui, offre donc l’échelle et la capacité opérationnelle. Pour les entreprises, cette alliance entre AWS et NEURA pourrait accélérer l’accès à des solutions robotiques plus fiables et plus rapidement déployables. Elle pose également les bases d’un nouveau modèle. Une IA physique connectée, évolutive et alimentée en continu par des données réelles. Ainsi, l’IA pourra s’incarner dans des machines qui interagissent avec le monde. Et avec ce type de partenariat, l’industrialisation de cette vision semble, enfin, à portée de main. Par ailleurs, NEURA construit pas à pas un écosystème international qui réunit acteurs de la robotique, industriels et spécialistes des semi-conducteurs. Parmi eux figurent notamment Kawasaki Heavy Industries, Bosch ou encore Qualcomm. Apparemment, NEURA veut créer une base technologique commune qui permettra aux robots d’apprendre plus vite et de s’adapter plus efficacement. Et aussi de générer de la valeur dans des secteurs variés, de l’industrie à la logistique, voire au domestique. À horizon 2030, NEURA évoque le déploiement potentiel de millions de robots cognitifs. IA 18 mai 2026 18 mai 2026 18 mai 2026 Votre adresse e-mail ne sera pas publiée. 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