AUTOMATION HISTORY

Sito di informazione indipendente Davide Amerio 16 luglio 2026 0 commenti 8.497 visite Redazione CDC 15 luglio 2026 0 commenti 7.940 visite Redazione CDC 14 luglio 2026 0 commenti 11.485 visite Redazione CDC 13 luglio 2026 0 commenti 15.416 visite Redazione CDC 27 giugno 2026 0 commenti 34.408 visite Redazione CDC 27 giugno 2026 0 commenti 11.908 visite Markus 14 luglio 2026 7 commenti 104.152 visite Redazione CDC 13 luglio 2026 7 commenti 94.785 visite Redazione CDC 14 luglio 2026 7 commenti 69.142 visite Redazione CDC 29 giugno 2026 0 commenti 59.700 visite Redazione CDC 27 giugno 2026 0 commenti 115.417 visite Markus 14 luglio 2026 7 commenti 104.152 visite Redazione CDC 13 luglio 2026 7 commenti 94.785 visite Redazione CDC 26 giugno 2026 6 commenti 79.823 visite Secondo il rapporto di Foreign Policy, Spot della Boston Dynamics sgombererà i dintorni di Kiev da granate e munizioni A cura di Massimo A. Cascone - pubblicato in data 27 giugno 2022 - 1 commento - 5.235 visite Secondo quanto riferito dal direttore esecutivo del gruppo Chris Whatley, che ha parlato con Foreign Policy, HALO Trust, un'organizzazione di sminamento con sede negli Stati Uniti che ha ricevuto diversi contratti governativi per lavorare in Ucraina, utilizzerà Spot - il cane robot di Boston Dynamics - per rimuovere mine, proiettili di mortaio e munizioni inesplose nelle aree precedentemente controllate dai russi vicino alla capitale Kiev. Whatley sostiene che in una sessione di test dello scorso anno, Spot ha "funzionato bene" nell'eliminazione di proiettili simili a quelli usati oggi in Ucraina. Dotato di un braccio robotico al posto della testa, il robot è in grado di raccogliere le munizioni inesplose, per farle poi brillare in modo sicuro, senza che ciò presenti alcun rischio né per i civili né per le squadre di HALO che attualmente lavorano in Ucraina. Grazei a Spot "si riesce a trasportare un oggetto senza mettere in pericolo un essere umano e a spostarlo abbastanza lontano da poterlo portare in un luogo dove può essere fatto esplodere in sicurezza insieme ad altri oggetti" ha dichiarato Whatley, aggiungendo che l'apporto del robot è fondamentale poiché gli sminatori necessitano di almeno sei settimane di addestramento e le risorse umane sono scarse. Foreign Policy sostiene però di non aver ricevuto alcun commento sulla questione né da Boston Dynamics, che sviluppa e costruisce i cani robot Spot, né dall'Esercito degli Stati Uniti, che avrebbe approvato il trasferimento. In un'e-mail inviata a Foreign Policy, il direttore marketing e comunicazione di Boston Dynamics, Nikolas Noel, ha ammesso che "in generale, Spot è uno strumento efficace per tenere le persone lontane dal pericolo, e il robot viene spesso utilizzato per ispezionare materiali potenzialmente pericolosi da una distanza di sicurezza". Tuttavia, Noel ha aggiunto che i termini e le condizioni di Boston Dynamics impediscono che il robot "venga utilizzato come arma o a scopo di offesa o intimidazione" e mentre Spot può essere addestrato a svolgere automaticamente diversi compiti semplici, gli ambienti imprevedibili del campo di battaglia fanno sì che possa essere utilizzato efficacemente solo se controllato manualmente da un operatore umano. Attualmente, secondo il viceMinistro degli Interni ucraino Meri Akopyan, sono circa 300.000 i chilometri quadrati di territorio ucraino che necessitano di essere bonificati dalle mine, e l'operazione potrebbe durare da cinque a dieci anni. A maggio, anche le Nazioni Unite hanno dichiarato che potrebbero essere necessari decenni per eliminare "le mine antiuomo e anticarro, così come altre munizioni inesplose o abbandonate lasciate in Ucraina", che "minacciano la vita di milioni di persone". Come abbiamo sostenuto precedentemente, l'Ucraina è un laboratorio a cielo aperto e anche la robotica, tra cani sminatori e droni d'attacco, vuole la sua parte. Massimo A. Cascone, 27.06.2022 Fonte: https://foreignpolicy.com/2022/06/22/ukraine-bomb-quad-robotic-dog/ Per commentare accedi all area riservata con un account abilitato. Accedi per commentare Registrati Teopratico 27 giugno 2022 È ingiusto che questi poveri cani-robot debbano rischiare la vita per noi umani, dovrebbe essergli garantita la razione quotidiana di energia come minimo, il minimo rischio e le cure meccaniche in caso di incidente... Scusate, non ho mai letto P.K. Dick, ma il tema mi perseguita! Valentina Bennati 25 giugno 2026 7 commenti 54.658 visite Valentina Bennati 6 giugno 2026 1 commento 45.393 visite Valentina Bennati 29 aprile 2026 1 commento 35.028 visite Valentina Bennati 26 marzo 2026 2 commenti 38.147 visite Redazione CDC 21 giugno 2026 14 commenti 32.749 visite Redazione CDC 26 giugno 2026 12 commenti 48.620 visite Redazione CDC 23 giugno 2026 10 commenti 24.807 visite Redazione CDC 21 giugno 2026 10 commenti 6.800 visite Utilizzando i servizi di comedonchisciotte.org accetti la nostra Netiquette il Disclaimer, le Condizioni d’uso e di partecipazione e la politica dei Cookies I contenuti di questo sito sono distribuiti con Licenza CC 4 Licenza Creative Commons Il sito non si assume alcuna responsabilità per gli articoli ed il materiale pubblicato I contenuti sono pubblicati senza periodicità, dai rispettivi autori. © 2026 Comedonchisciotte.org Crediti & Copyrights Cari lettori e commentatori, stiamo lavorando per ripristinare il normale funzionamento del portale web dopo il grave attacco hacker subito. 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Don't have an account? Subscribe “Our goal is to help stakeholders understand the future of mobility.” Home › News › Hyundai takes full control of robot firm Boston Dynamics Ageing labour forces and EV assembly precision, not novelty, are the real reasons Hyundai is gambling on humanoid robots. By Stewart Burnett Hyundai will take full ownership of US robotics firm Boston Dynamics by acquiring SoftBank’s remaining stake of around 10%, a deal valued at roughly KRW 500bn (US$335m). The move follows Hyundai’s initial 80% acquisition of the robotics pioneer in 2021 and clears the way for the group to align Boston Dynamics entirely with its own automotive manufacturing priorities. The centrepiece of that plan is Atlas, Boston Dynamics’ humanoid robot, and the production-ready version was unveiled at CES 2026. Hyundai plans to first deploy Atlas at its Georgia electric vehicle (EV) plant starting in 2028, initially on parts sequencing and heavy lifting, before expanding its role to welding and component assembly—and deploying it in other plants—by 2030. With Boston Dynamics now wholly-owned, Hyundai is building capacity to produce up to 30,000 Atlas units annually, with plans to deploy more than 25,000 across its own and Kia’s plants while selling the remainder to external customers. It boasts multiple apparent advantages over its human counterparts, including the ability to rotate its torso through a full 360 degrees. The robot can also autonomously navigate to charging docks to swap its own batteries and lift 30 kg on a sustained basis. Full ownership also removes the last structural friction in Hyundai’s plan: a minority shareholder with its own return expectations and timeline can no longer complicate decisions about how aggressively Boston Dynamics’ technology gets folded into the automaker’s factories. The underlying driver is demographic and industrial rather than purely technological. Indeed, ageing labour forces and rising manufacturing costs across Hyundai’s key markets, combined with the precision demands of EV assembly, have made a scalable substitute for repetitive, physically punishing tasks a genuinely compelling solution. Hyundai’s ownership also lets it fold Boston Dynamics’ robotics expertise directly into its existing supply chain and innovation network. Hyundai Mobis, which functions as both the group’s parts division and a software development unit, is set to manufacture Atlas’s custom actuators, lowering production costs and keeping a critical component in-house. At the same time, live car factories can provide the kind of closed-loop, real-world testing data that pure research and development environments are incapable of replicating. The biggest complication may not come from the robot’s efficacy but local resistance from Hyundai’s labour unions. Korean workers, already in the midst of threatening strike action against the automaker, have explicitly flagged displacement and potential loss-of-hours as concerns at the bargaining table as negotiations continue. Back in January, the union warned that “not a single robot can be deployed at worksites without an agreement between the union and management.” Of course, Hyundai isn’t the only automaker investing in humanoid robots. Tesla has grabbed far more headlines with its Optimus robot, arguably the centrepiece of a company-wide ‘physical AI’ rebranding. In Tesla’s case, it is the robot’s AI—more so than its physical hardware—that is positioned as the real product. Tesla leans on neural networks and imitation learning drawn from its Full Self-Driving architecture, including the AI4 and AI5 chips, and is testing Optimus directly inside its own Texas and Fremont plants. An aggressive production timeline is planned for Optimus, with production set to begin in August 2026 and scaling towards mass production of up to one million units annually the following year. With that said, Tesla’s promises around AI and automation—particularly when a firm timeline or large production volumes are involved—should be taken with a pinch of salt. Xpeng’s Iron humanoid robot serves as China’s answer to Atlas and Optimus, built on a fully self-developed, in-house chip and software stack designed for agility and cost efficiency rather than raw physical capability. The robot is intended for more public-facing, arguably flashier, use-cases than heavy manufacturing—for instance, customer-facing retail roles like showroom assistance. Hyundai’s Atlas, by contrast, is being deployed cautiously into structured, back-of-house industrial tasks first, reflecting Boston Dynamics’ decade-plus head start in mechanical engineering and control theory. The Georgia deployment in 2028 is where that hardware-first bet will be tested against reality. Whether Atlas can move from controlled demonstrations to reliable performance on a live production line will determine whether the leap to genuine component assembly by 2030 is achievable, or whether Hyundai’s hefty investments amount to little more than expensive hot air. July 16, 2026 July 16, 2026 July 16, 2026 Let us help you understand the future of mobility "*" indicates required fields Δ July 3, 2025 Automotive World reviews Nissan’s recent market and production performance and provides detailed vehicle production forecasts through 2030. 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Posts from this topic will be added to your daily email digest and your homepage feed. See All Tech Posts from this topic will be added to your daily email digest and your homepage feed. See All Gadgets Posts from this topic will be added to your daily email digest and your homepage feed. See All News Can a $74,500 Spot robot find your front door? Can a $74,500 Spot robot find your front door? Posts from this author will be added to your daily email digest and your homepage feed. See All by Andrew Liszewski Posts from this author will be added to your daily email digest and your homepage feed. See All by Andrew Liszewski Boston Dynamics’ robotic quadruped Spot has already found work doing routine factory inspections and patrolling the ruins of Pompeii, but what about deliveries? The company is testing a new conveyor belt accessory that allows Spot to carry packages from a vehicle and autonomously unload them on a customer’s doorstep in an effort to reduce a delivery driver’s workload. While attempts have already been made to expedite and automate deliveries using wheeled robots or aerial drones, humans are still the most efficient way to navigate obstacles like stairs or cluttered pathways in order to get packages from a truck to a doorstep. A demo video shows a driver unloading packages onto Spot for doorstep distribution that Boston Dynamics hopes will be efficient enough to justify buying one of the $75k~ bots. Boston Dynamics says it’s “already in talks with major logistics companies about testing Spot for a last-mile delivery solution” and its current goal is to move from these demos to a full pilot project. Spot’s unique ability to clamber over uneven terrain and navigate complicated obstacles could facilitate its use as a delivery assistant. The robot is already frequently used to help comb through rubble as part of search and rescue operations, and Boston Dynamics believes those capabilities could make it well suited for navigating the “unstructured environments of suburban neighborhoods” where the path from the curb to a front door is rarely obstacle-free. The robot will not only help reduce physical strain on delivery drivers, it will also give them more time for other tasks such as prepping the next delivery while Spot is dropping off its current payload, potentially increasing their capacity. “For every three packages delivered via Spot, we think we can add another package to the van,” says Paige Miller, Boston Dynamics’ senior staff product manager for Spot. Posts from this author will be added to your daily email digest and your homepage feed. See All by Andrew Liszewski Posts from this topic will be added to your daily email digest and your homepage feed. See All Gadgets Posts from this topic will be added to your daily email digest and your homepage feed. See All News Posts from this topic will be added to your daily email digest and your homepage feed. See All Robot Posts from this topic will be added to your daily email digest and your homepage feed. See All Tech A free daily digest of the news that matters most. This is the title for the native ad This is the title for the native ad © 2026 Vox Media, LLC. All Rights Reserved Sign in to see your notifications or create an account to join the conversation.

Firma UBTech Robotics, znana dotąd z rozwiązań przemysłowych i usługowych, zaprezentowała nową linię bionicznych robotów humanoidalnych UWORLD U1. To strategiczny krok w stronę rynku konsumenckiego, ze szczególnym uwzględnieniem opieki zdrowotnej, wsparcia psychologicznego oraz interakcji emocjonalnych. 30 czerwca 2026 roku chińska firma UBTech Robotics Corp. Ltd., notowana na giełdzie w Hongkongu, oficjalnie zaprezentowała nową serię robotów oznaczonych symbolem U1. Wydarzenie to wyznacza nowy kierunek w działalności przedsiębiorstwa, które dotychczas koncentrowało się na sektorze B2B, dostarczając przemysłowe roboty humanoidalne do fabryk i centrów logistycznych. Nowa linia U1 została zaprojektowana z myślą o konsumentach indywidualnych. Kierownictwo UBTech określa ten ruch jako budowę “drugiej krzywej wzrostu” firmy. Roboty mają docelowo pełnić funkcje „towarzyszy emocjonalnych”, asystentów domowych oraz wsparcia w opiece nad osobami starszymi i wymagającymi pomocy psychologicznej. Seria UWORLD U1 składa się z trzech podstawowych modeli, różniących się stopniem zaawansowania mechanicznego i ceną: Pełnowymiarowe modele mają 183 cm (wersja męska) lub 168 cm (wersja żeńska) wysokości i ważą około 42 kg. Konstrukcja robota opiera się na mechanizmie posiadającym 88 stopni swobody (DoF), co umożliwia płynne i naturalne ruchy. Urządzenia są zasilane akumulatorowo, a deklarowany czas pracy na jednym ładowaniu przekracza cztery godziny. Inżynierowie UBTech położyli duży nacisk na realizm fizyczny robotów serii U1. Powłoka zewnętrzna została wykonana z silikonu imitującego ludzką skórę, na której odwzorowano detale takie jak unaczynienie. Skóra robotów nie jest zimna, utrzymuje temperaturę zbliżoną do temperatury ludzkiego ciała, a wbudowane czujniki pozwalają im reagować na dotyk, siłę uścisku oraz puls użytkownika. Mechanizm twarzy umożliwia generowanie ponad 30 różnych rodzajów mimiki, a system wizyjny pozwala na śledzenie rozmówcy wzrokiem. Oprogramowanie sterujące robotami oparto na wyspecjalizowanym modelu sztucznej inteligencji, określanym przez producenta jako “model służący do rozwoju emocjonalnego”. System ten wyposażono w pamięć kontekstową, co pozwala robotowi na zapamiętywanie historii konwersacji i personalizację interakcji z konkretnym użytkownikiem. Dodatkowo, UBTech oferuje aplikację UWorld, w której robot posiada swojego cyfrowego awatara, umożliwiając kontynuację interakcji poza domem. Firma poinformowała o dużym zainteresowaniu nowym produktem na etapie przedsprzedaży. Model U1 Pro, dostępny po wpłaceniu zaliczki w wysokości 3000 RMB (ok. 1710 PLN), zebrał ponad 13 000 zamówień przed oficjalną premierą. Dla porównania: w całym 2025 roku UBTech sprzedał łącznie około 1100 pełnowymiarowych robotów przemysłowych i usługowych. Założyciel i prezes UBTech, Zhou Jian, zadeklarował, że celem firmy na 2026 rok jest realizacja od 30 000 do 50 000 zamówień na urządzenia z serii U1. W pierwszej kolejności firma skupi się na terminowym dostarczeniu początkowych 10 000 egzemplarzy. Masowa wysyłka modelu U1 Pro do klientów ma rozpocząć się 16 września 2026 roku. Strategia biznesowa UBTech dla serii U1 zakłada dywersyfikację źródeł przychodu. Oprócz jednorazowej sprzedaży sprzętu, firma planuje generować wpływy z subskrypcji oprogramowania oraz usług serwisowych. Wśród potencjalnych obszarów zastosowań wymienia się opiekę geriatryczną, doradztwo medyczne oraz wsparcie psychologiczne. UBTech analizuje również możliwość wejścia na rynek gier typu otome (symulacji romantycznych), prowadząc rozmowy z właścicielami praw do popularnych marek z tej branży. Wprowadzenie wysoce realistycznych robotów humanoidalnych do przestrzeni domowej wiąże się z wyzwaniami natury społecznej i etycznej. Aby adresować te obawy, UBTech powołał wewnętrzną komisję etyczną ds. sztucznej inteligencji i robotyki. Jednocześnie na chińskim rynku rośnie konkurencja w segmencie robotów humanoidalnych. UBTech musi rywalizować z takimi podmiotami jak Noetix Robotics, Digit Technology czy Droid Robotics, które również rozwijają własne platformy bioniczne. Autor: 梁安基 Andrzej Z. Liang, 上海 Shanghai, 中国 Chiny e-mail: azliang@chinamail.com Redakcja: Leszek B. Ślazyk Wiadomość o premierze UBTech U1 dotarła do mnie tego samego dnia, w którym obejrzałem „Ghost in the Shell 2: Innocence”, japoński film animowany z 2004 roku, którego scenariusz napisał i reżyserował Mamoru Oshii. Kilka dni wcześniej obejrzałem w kinie „Ghost in the Shell” z 1995 roku. To pewnie początek poważniejszej przygody z japońską mangą i animacją, z którymi do tej pory kontakt miałem niewielki, ograniczający się do nabywania komiksów synom. Zwłaszcza ten drugi chronologicznie film wybrzmiewa dziś dojmująco profetycznie. Tak, doszliśmy do momentu, w którym można już produkować i sprzedawać maszyny udające ludzi. Jeszcze nieporadnie, jeszcze bardzo mechanicznie. Ale już jest na nie ogromny rynek. I będzie się powiększać. Z wszystkimi tego konsekwencjami, wśród których trudno mi znaleźć definitywnie dobre. Przynajmniej w sytuacji braku prawnych norm dla samych maszyn, jak i dla ich funkcjonowania w świecie. Mamoru Oshii przedstawił w 2004 roku większość tych konsekwencji wynikających z konfrontacji ludzkich przywar i słabości z możliwościami, które otwierają nowe technologie robotyczne. Z drugiej zaś strony… Ciekawe, czy na kolejnych etapach rozwoju technologii cyfrowych pojawi się realna możliwość dygitalizowania nas samych… Jakie konsekwencje niosłaby taka możliwość? Pytać można, gdybać można, nieprawdaż? Nie wiadomo tylko, czy na drodze do dalszego rozwoju nie stanie, tudzież nie położy się na przykład brak wody, który ponoć już teraz stanowi wyzwanie dla nowych centrów danych. Oj, lubimy sobie jako ludzkość robić krzywdę. Lubimy. Źródła Leszek B. Ślazyk e-mail: kontakt@chiny24.com © www.chiny24.com […] algorytmu HG-STR wpisuje się w szerszy trend inten... […] o rosnącej rentowności firm z tego kraju, które zd... […] ta wpisuje się w szerszy trend transformacji branż... […] podejście nie jest przypadkowe. Chińskie społeczeń... […] i podważają fundamenty naukowej rywalizacji. Tłem... […] algorytmu HG-STR wpisuje się w szerszy trend inten... […] o rosnącej rentowności firm z tego kraju, które zd... […] ta wpisuje się w szerszy trend transformacji branż... […] podejście nie jest przypadkowe. Chińskie społeczeń... […] i podważają fundamenty naukowej rywalizacji. Tłem... Study Tours do Chin Autorskie wyjazdy poznawcze z Leszkiem Ślazykiem - technologia, AI, roboty, biznes. Cztery formaty, zgłoszenia indywidualne. Sprawdź, który jest dla Ciebie.

Company reports more than 10,000 orders and launches an initiative to donate 100 humanoid robots in support of mental well-being programs SHENZHEN, China, July 1, 2026 /PRNewswire/ -- On June 30, 2026, UBTECH hosted its 2026 Global Launch Event in Shenzhen, where it introduced its long-term vision for human-robot symbiosis and officially unveiled the UWORLD U1 Series of full-size ultra-bionic humanoid robots, the first in the world designed for mass production. The lineup includes three models: the U1 Lite semi-torso edition, the high-performance full-body U1 Pro, and the high-dynamic full-body U1 Ultra, with pricing set from 119,800 RMB. As of the day of the launch event, cumulative orders for the UWORLD U1 Series had surpassed 13,361 units. UBTECH Launches UWORLD U1, the World's First Full-Size Mass-Produced Ultra-Bionic Humanoid Robot Zhou said UBTECH is evolving beyond a traditional robotics manufacturer toward a platform company focused on AI-enabled robotics and intelligent infrastructure. He noted that the company has pursued a two-decade roadmap for growth. During the first phase (2012-2022), UBTECH focused on developing core technologies, helping bring humanoid robots from research laboratories into industrial deployment while building the company's commercial foundation. During the second phase (2023-2033), the company is expanding into consumer robotics with the goal of making humanoid robots part of everyday life. The year 2026 marks an important step in the evolution of UBTECH from industrial and commercial applications toward consumer adoption. Backed by its self-developed full-stack humanoid robot technologies, broad application portfolio, and continued investment in innovation and talent, UBTECH has achieved multiple industry milestones. Building on its industrial robotics business, the company ranked 1st globally in revenue and sales volume from full-sized embodied intelligent humanoid robots in 2025, and launches UWORLD in 2026, extending its robotics portfolio into the consumer market. Michael Tam, Chief Brand Officer of UBTECH, President of Consumer Robotics Innovation Business Group, and General Manager of UWORLD, said human-robot companionship represents a new approach to supporting mental well-being. He added that companion robots have the potential to become an important new consumer category by providing personalized emotional support throughout different stages of life. He cited projections that China's ultra-bionic humanoid robotics market could grow from the tens of billions of RMB to the trillion-RMB level between 2026 and 2036. UWORLD integrates a fully proprietary end-to-end technology stack, including biomimetic skin, embodied intelligence hardware, operating systems, emotion-driven large language models (LLMs), and system-level manufacturing. This integrated architecture is designed to address long-standing challenges in the real-world deployment of humanoid robots. The UWORLD U1 Series features 88 degrees of freedom and a proprietary dual-pivot biomimetic cervical spine, enabling it to replicate up to 90% of fundamental human movements. The system is powered by the world's first emotion-aware LLM designed for long-term companionship, capable of recognizing more than 20 fine-grained emotional states with an accuracy rate exceeding 90%. Its biomimetic fast-and-slow brain architecture draws on cognitive neuroscience principles, enabling a 500-millisecond intuitive response system alongside deep reasoning capabilities powered by models with hundreds of billions of parameters. A biomimetic expression actuation system, driven by a proprietary controller, reduces speech-to-lip synchronization latency to within 20 milliseconds, creating a remarkably lifelike interaction experience. The Agent Memory OS, a cross-temporal memory system, is designed to establish a persistent digital life framework. A proactive care engine uses environmental awareness to enable wake-word-free interaction, allowing UWORLD robots to respond naturally to contextual cues and social situations. UWORLD also emphasizes the principle that users retain ownership of their data and has established a three-layer privacy architecture consisting of local-first processing, minimal cloud dependency, and user-controlled hardware safeguards. Combined with highly lifelike interaction capabilities and an end-to-end safety framework, the system enables humanoid robots to evolve from functional tools into emotionally responsive companions. The U1 Series is designed to support a wide range of consumer and commercial applications, including daily companionship, emotional support, lifestyle enhancement, and social assistance, as well as reception and hospitality services, elder care, psychological support, tourism and exhibitions, research and education, and premium domestic service applications. At the launch event, UWORLD introduced the "Human-Robot Companionship Initiative." China has more than 90 million adults living alone and 118 million empty-nest seniors, with an estimated 10% to 20% of individuals living alone meeting the clinical criteria for mental health disorders. In response, UWORLD announced plans to donate customized humanoid robots each year to support vulnerable groups, including children growing up apart from one or both parents, older adults living alone, and families facing difficult circumstances, with the goal of providing long-term emotional companionship and psychological support. In 2026, UWORLD plans to donate 100 customized U1 Series humanoid robots. These units will incorporate 3D facial reconstruction and voiceprint-based identity replication technologies to recreate designated individuals, while integrating emotion-driven interaction models and dedicated long-term memory systems. Combined with multimodal situational awareness, the robots are designed to provide structured psychological support services. With the launch of UWORLD, UBTECH has established a robotics portfolio spanning industrial, commercial and consumer applications. The industrial Walker S Series has already entered mass production and commenced deliveries, while UWORLD is expected to become the company's second growth engine as it brings humanoid robotics to consumer markets. About UBTECH Established in March 2012, UBTECH ROBOTICS CORP LTD is a leading humanoid robot and smart service robot company. Specialized in the research, development, and commercialization of intelligent service robots and AI-driven solutions, its product portfolio spans industrial humanoid robots (Walker series), AI Education, Smart Logistics, Commercial Service, and Consumer Service. Milestones include mass production of the Walker S2 humanoid robot and listing on the HKEX. UBTECH is committed to advancing embodied AI to build a smarter, more connected future. For more information, please visit https://www.ubtrobot.com/en/ UBTECH Launches UWORLD U1, the World's First Full-Size Mass-Produced Ultra-Bionic Humanoid Robot UBTECH Launches UWORLD U1, the World's First Full-Size Mass-Produced Ultra-Bionic Humanoid Robot

BMW Group is taking another major step toward AI-driven manufacturing by introducing the next-generation Figure 03 humanoid robot at its Spartanburg, South Carolina production facility. The deployment builds on the success of the earlier Figure 02 robot and marks another milestone in BMW’s strategy to integrate Physical AI into real-world vehicle production. The latest robot will be assigned to logistics operations, where it will perform complex sequencing tasks that help ensure assembly components arrive at workstations in the correct order and at the right time. Plant Spartanburg has become BMW’s primary testing ground for humanoid robotics. During a 10-month pilot program in 2025, the Figure 02 robot worked in the body shop, inserting sheet-metal components for welding during production of more than 30,000 BMW X3 vehicles. The trial demonstrated that humanoid robots can safely carry out repetitive, precision-based manufacturing tasks under real production conditions while reducing the physical strain on employees. Following those results, BMW is now expanding the technology with the more capable Figure 03 platform. According to Figure AI, the new robot introduces several hardware improvements designed to broaden its industrial applications, including: – Soft exterior components for improved workplace safety. – Wireless charging for increased operating availability. – Speech-to-speech communication capabilities. – Upgraded robotic hands featuring tactile sensors and palm-mounted cameras for greater precision and dexterity. These upgrades allow Figure 03 to tackle more advanced manufacturing and logistics tasks than its predecessor. The robot’s first assignment will focus on sequencing components inside the logistics center. Parts initially arrive in large, unsorted containers. Figure 03 will identify and pick individual components before organizing them into sequencing trolleys. Those trolleys are then transported by automated tugger trains or Smart Transport Robots to the assembly line, ensuring workers receive the correct parts exactly when they are needed. This “just-in-sequence” process is widely used in automotive manufacturing and offers significant opportunities for automation and future scalability. BMW views humanoid robots as an extension of its existing automation systems rather than a replacement for employees. The company says Physical AI has the greatest potential in repetitive, ergonomically demanding, and safety-critical tasks. By assigning these activities to intelligent robots, BMW aims to improve workplace safety, reduce employee fatigue, and allow workers to focus on more complex responsibilities. The deployment of Figure 03 also aligns with BMW’s broader iFACTORY production strategy. Hall 52 at Plant Spartanburg—where the BMW X3 is assembled and the upcoming all-electric BMW iX5 will eventually be built—has undergone a significant digital transformation. Virtual 3D simulations are used to optimize production processes before equipment is installed, while the BMW Virtual Factory helps engineers refine workflows and improve employee ergonomics. Artificial intelligence also plays a growing role in quality control. BMW’s AIQX (Artificial Intelligence Quality Next) system uses cameras, sensors, and acoustic analysis to detect production issues in real time, providing immediate feedback to line workers through smart devices. The company is also evaluating making the technology available to suppliers. With Figure 03 entering daily operations, BMW continues to demonstrate how humanoid robots are evolving from experimental technology into practical manufacturing tools. As AI, robotics, and digital factory technologies continue to mature, BMW is positioning its production network to become more flexible, efficient, and resilient while supporting employees with intelligent automation rather than replacing them. Blagojce Krivevski is physicist and green technology lover. Keep in touch with Blagojce through his email, web site, Twitter, Linkedin, Facebook and Google+. Email: contact@electriccarsreport.com

Teknoloji ve bilim dünyasını seven ve takip etmekten büyük zevk alan Metin, öğrendiklerini ise DonanımHaber okuyucuları ile paylaşır. Figure AI'ın açıklamasına göre Figure 03, Spartanburg tesisindeki montaj ve lojistik faaliyetlerinin yürütüldüğü Hall 52 bölümünde görev yapmaya başladı. Burada robot, klasik parça taşıma görevlerinin ötesine geçerek otomotiv üretiminin kritik aşamalarından biri olan parça sıralama operasyonlarını yerine getiriyor. Figure AI CEO'su Brett Adcock ise Figure 02'nin 11 aylık kullanımının insansı robotların artık laboratuvar projeleri olmaktan çıktığını gösterdiğini ve esnek üretim iş gücünün önemli bir parçası haline gelebileceğini söyledi. BMW'den menzil rekoru: Yeni iX5 tam 845 km menzille tanıtıldı! 1 hf. önce eklendi Hall 52 bölümünde halihazırda BMW X3 modelleri üretilirken yakında elektrikli BMW iX5 modeli de üretilecek. Dolayısıyla Figure 03, BMW iX5’in üretiminde pay sahibi olacak. Figure 03 yalnızca parçaları kavrayıp bırakmanın yanı sıra tekerlekli ağır metal taşıma arabalarını çekebiliyor. Robot hassas parçaları yüksek doğrulukla alıp doğru bölmelere yerleştirirken gerektiğinde tüm vücudunu kullanarak yüksek kuvvet gerektiren işlemleri de gerçekleştirebiliyor. Şirkete göre bu tür dinamik malzeme manipülasyonu, geleneksel sabit otomasyon sistemleri veya altı eksenli robot kollarıyla yapısal olarak çözülmesi zor görevler arasında yer alıyor. Bir diğer zor görev ise parça sıralama süreci. Zira büyük konteynerler içinde düzensiz şekilde gelen parçaları algılayıp doğru sırayla özel taşıma arabalarına yerleştirmek gerekiyor. Figure 03, , Helix 02 yapay zekası sayesinde çevresini sürekli analiz ederek hareketlerini anlık olarak ayarlayarak değişken koşullara uyum sağlayabiliyor. Figure 03, önceki nesle göre çeşitli donanımsal iyileştirmeler de içeriyor. Robotta güvenliği artırmaya yönelik yumuşak bileşenler, kablosuz şarj desteği, konuşmadan konuşmaya iletişim sağlayan ses özellikleri, dokunsal sensörlerle geliştirilen yeni eller ve avuç içine yerleştirilen kameralar bulunuyor. Bu yenilikler sayesinde robotun çevikliği de artıyor. Ultra gerçekçi arkadaş robot kapışılıyor! {{Description}} https://www.amazon.com.tr/dp/B00UST658E https://www.amazon.com.tr/dp/B0F9YWT6RK https://www.amazon.com.tr/dp/B0F1DNR44Q https://www.amazon.com.tr/dp/B0DJP7ZLRN https://www.akbank.com/icerik/akbank-mobil-seyahat-paketi-kampanyasi https://amzn.eu/d/0ccbaW3u

ByMichael Ashley, Contributor. Executives often overlook embodied AI (EAI), focusing instead on digital models, despite its imminent rise. Chris Chen of Faraday Future highlights EAI's need to master real-world physics like gravity and balance, a stark contrast to digital AI's text-based learning. Training EAI is profoundly difficult due to countless physical scenarios and the "sim-to-real gap," where simulated success rarely translates perfectly. Therefore, real-world physical data collection is paramount, with Faraday Future engaging youth in robotics to gather this crucial information. This shift will disrupt blue-collar jobs, much like digital AI impacted white-collar roles. Leaders, as Nvidia's Jensen Huang suggests, must prepare for AI that extends beyond screens into our physical reality. Picture a company CEO obsessed with building a Large Language Model (LLM), thinking this is the way to win the intelligence race. Monday mornings she grills her team on variables like benchmark scores, token costs and hallucination rates. Every hard-won incremental improvement feels like a triumph. “We are really building something special,” she cheers her staff on. But in all that time, a single question never gets asked, one that might give our CEO a real edge in the AI Age: how will it learn to pick up a pen? Across corporate America, many executives still think like our fictional CEO. Myopically, they focus on digital AI, the kind that lives on your browser, phone, or tablet. Chris Chen, Co-CEO of Faraday Future, an embodied artificial intelligence (EAI) company, believes that’s the wrong way to prepare for the future that’s fast materializing. “The whole world is on the cusp of physical AI,” he told me in an interview at the official launch of the company’s EAI Robotics Education Ecosystem in Los Angeles. Here his organization demoed various models of its robotics line, which Faraday Future brands collectively as “EAI robots,” including its second-generation flagship full-size humanoid robot and FX Navi, a micro quadruped EAI robot for family and school-based AI education. The event also offered a preview of two more humanoid models still awaiting full launch: Master Mini, developed for youth education and athletic competition, and Nova, a smaller companion robot for children. Chen’s point is that digital AI was trained on “a massive amount of data—billions or trillions of words from books, articles, websites, code and other text sources,” according to IBM.com. Over time, models learned to predict, reason, and detect patterns, leading to major gains in artificial intelligence, especially the generative type that has roiled the white-collar sector, prompting Anthropic CEO Dario Amodei to suggest “AI could wipe out roughly 50% of all entry-level white-collar jobs,” per Fortune. But all that data ingestion has mostly enhanced disembodied AI. Impressive as that is, and it is a stunning feat, there’s a world of difference between two-dimensional digital screens and AI that can act and move in a three-dimensional world. To put it in more concrete terms, an AI that can describe the sound of an egg breaking using Faulknerian language is helpful. A robot with an AI brain that can crack those eggs to make your fiancé a souffle signifies a sea change executives may ignore at their peril. It sounds odd to suggest it, but digital AI learning is almost easy compared to what’s required for embodied AI. “A robot must face countless real-world scenarios,” says Chen. “That's much more difficult, especially when it comes to navigating forces like gravity, balance, friction and uncertainty. Embodied AI must understand the laws of physics.” Knowing this, Chen suggests the coming bottleneck isn’t intelligence as it was throughout much of the 21st century while AI came online. Rather, it’s acquiring sufficient real-world data. After all, you cannot build embodied AI quadrupeds that work well enough for the consumer market to embrace them without sufficient data to inform their locomotion and behavior. To reiterate, ChatGPT, Claude, Gemini, Grok and other frontier models were trained on copious amounts of text and images. The Robotic Revolution that I previewed in a past Forbes article can only take off if companies train embodied AI on the myriad physical activities developing humans like babies and toddlers take for granted: touching, walking, carrying, failing and of course, recovering. Okay, fine, you might be thinking. Then why don’t we just trains robots using simulations? We train human pilots that way. Not so fast. According to training-data company Claru.ai, the “sim-to-real gap” is real. And gnarly. “Differences in visual rendering, contact physics, sensor noise, and actuator dynamics between simulator and real-world cause policies to encounter conditions they never saw during training, leading to failures that range from reduced accuracy to complete task breakdown.” This same source suggests robots enjoying a 90% success rate in synthetic environments may only achieve a 30-60% performance rate in the real world. “Being first to deliver robots to real users matters because the latter will generate the data that improves future robots,” says Chen. We can see this truism with the example of self-driving cars. A consumer with only a facile understanding of the electric vehicle charging in their garage may be excused for merely appreciating the fact they don’t have to pay for gas anymore. What they may not realize is that such vehicles are “more than meets the eye,” to borrow a line from Transformers. They are, in fact, data collectors. “Tesla achieves their self-driving features through AI/Machine Learning combined with an incredible system of data acquisition and aggregation from various sources. All Tesla models are equipped with sets of sensors, cameras, and radars, that constantly collect data from individual cars’ detailed surroundings from the outside and driver and passenger gestures within the car,” explains Harvard Digital Innovation and Transformation. So where might such all-important physical data collection come from in the future? Our youth. To this end, Faraday Future has invested in a strategic two-way partnership with Triple I, an education institution, to promote summer camps where young people learn the types of hands-on robotics skills needed for tomorrow’s workforce, while at the same time students contribute to embodied AI’s physical understanding of the real world. While there are real-world benefits to such training, it would be disingenuous to suggest that there aren’t economic risks to such technological disruption. Just as digital AI has disrupted the white-collar workforce, as Amodei acknowledged, it is troubling to recognize that some blue-collar human workers are already training their robotic replacements. “The latest upheaval at Amazon comes from the rollout of advanced warehouses outfitted with robots. The company is overhauling older facilities to reduce their worker headcount,” according to Dissent Magazine. How automation ends up affecting humans working at factories, not just at Amazon, but globally, remains to be seen. For now, the key insight for executives everywhere should be not just to focus on training for digital AI, but also to prepare for the day when embodied AI takes off. None other than Nvidia CEO Jensen Huang has publicly stated that if he were a 20-year-old today, he would focus on the physical sciences as the next big opportunity. Huang told CNBC, “The next wave requires us to understand things like the laws of physics, friction, inertia, cause and effect.” Huang’s remarks, coupled with Chen’s, underscore a broader shift in thinking no CEO should dismiss. If the last few years have taught us anything, it’s that AI is fast becoming an essential technology like the internet, and before that electricity. To keep up with the pace of rapid change, executives everywhere need to make the leap from digital to physical, anticipating a world where AI isn’t limited to your screen but is part of your living, breathing everyday reality.

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. Reflect v1.0 lets users assign or change robot missions using natural-language prompts, even during execution. Flexion Robotics has introduced Reflect v1.0, a robotics intelligence platform that enables humanoid robots to complete complex, multi-step tasks autonomously, without human intervention during execution. The company showcased the system through a workplace delivery demonstration in which a humanoid robot retrieved a snack parcel, navigated stairs and an elevator, unpacked the box, and stored the items in a drawer. According to Flexion, Reflect v1.0 integrates mission control, motion planning, whole-body control, and runtime software, with reinforcement learning boosting task completion in an internal 16-step mission evaluation from 38 percent to 90 percent. Recently, ShengShu Technology unveiled Motubrain, a unified AI model that combines perception, reasoning, and action to provide a general-purpose brain for robots. Reflect v1.0 is a robotics intelligence platform designed to enable humanoid robots to autonomously complete long, multi-step missions in environments built for humans. The company says the platform combines advanced reasoning, perception, motion planning, and whole-body control to allow robots to perform complex tasks without human intervention during execution. To demonstrate the system, Flexion assigned a humanoid robot a single natural-language instruction: retrieve a snack parcel delivered to the building, use the stairs and elevator to transport it, unpack the box, and place the contents in a designated drawer. The robot completed the mission autonomously, navigating multiple floors, interacting with doors and elevators, opening the package with tools, storing the items, and adapting when unexpected situations occurred. According to Flexion, long-horizon autonomy remains one of the biggest challenges in robotics because even highly reliable navigation, manipulation, and perception systems can collectively fail when errors accumulate across many sequential tasks. Reflect v1.0 addresses this by integrating a custom vision-language model (VLM) that acts as a mission controller, continuously monitoring progress, reasoning about the environment, and replanning whenever necessary. The platform combines this mission controller with a motion layer that uses vision-language-action models trained on real-world data alongside reinforcement learning-based skills. These components translate visual observations into navigation, object manipulation, and environmental interactions, while a real-time whole-body controller maintains balance, stability, and precise movements throughout the mission. An optimized runtime system manages communication, low-latency inference, safety monitoring, and process coordination across multiple computing devices. A key feature of Reflect v1.0 is its use of natural-language instructions rather than task-specific programming. Users can modify missions simply by changing the prompt, allowing robots to perform different tasks or even receive updated instructions while a mission is already underway. Flexion said reinforcement learning has significantly improved the platform’s reliability. In an internal evaluation involving a 16-step mission, a supervised fine-tuned model achieved only a 38 percent end-to-end completion rate. After reinforcement learning was applied across multiple layers of the system, completion rates increased to 90 percent. The company also highlighted advances in manipulation and mobility. Robots trained with Reflect can handle boxes weighing between 100 grams and 3.5 kilograms, reposition packages using coordinated whole-body movements, operate elevators, use tools, traverse stairs repeatedly, navigate uneven terrain, and avoid dynamic obstacles while carrying objects. When failures occur, such as a missed grasp or displaced object, the robot can retry actions locally or replan the overall mission using feedback from its onboard cameras. Despite the progress, Flexion acknowledged that Reflect v1.0 remains limited to defined task distributions and does not yet provide universal autonomy. The company said future development will focus on expanding skill diversity, improving failure recovery, strengthening simulation-based training, and advancing end-to-end mission reasoning for broader real-world deployment. 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

Si votre email correspond à un compte, vous recevrez un lien de réinitialisation. La firme SwitchBot, déjà bien connue pour ses gadgets malins qui automatisent la maison, fait un pari audacieux. Elle s'attaque à un besoin profondément humain : la compagnie. Avec Noa, le modèle blanc décrit comme énergique, et Niko, le gris plus timide, l'entreprise veut offrir l'expérience affective d'un animal de compagnie, mais sans les contraintes logistiques. L'idée est de créer un lien durable avec une machine qui évolue et s'adapte à son propriétaire. Le secret de ces nouveaux robots compagnons ne réside pas seulement dans leur apparence de manchots en peluche. Le vrai coup de génie technique, c'est leur autonomie. Tout tourne en local. Pas de Wi-Fi, pas de problème. La firme a intégré un LLM (un grand modèle de langage, le même type de moteur que l'intelligence artificielle de ChatGPT) directement dans le ventre de ses créatures. Cette IA embarquée leur permet de tenir des conversations, de réagir en temps réel et d'apprendre sans dépendre d'une connexion cloud permanente. Cette technologie leur permet une interaction émotionnelle poussée. Grâce à des caméras, des micros et 12 points de contact sensibles, Noa et Niko peuvent détecter le ton de votre voix, vos gestes et même reconnaître les différents membres de la famille. Ils vous suivent dans la maison, vous accueillent à la porte et leurs yeux LCD expriment une palette d'émotions. Une véritable simulation de présence affective, calculée au millimètre. Un robot qui vous filme et vous écoute, ça a de quoi inquiéter. SwitchBot semble avoir anticipé le problème et propose une solution plutôt astucieuse : un masque physique. En le plaçant sur les yeux du robot, on active un "mode privé" qui désactive instantanément la caméra et la capture d'images. Une solution simple et visuelle pour rassurer l'utilisateur. De plus, les photos prises à la demande (en faisant un signe "peace" ou en disant "cheese") sont stockées localement dans l'application compagnon. Le robot tient aussi un "journal intime" de ses interactions, mais ces données restent sur l'appareil. Une approche qui cherche à trouver un équilibre entre personnalisation et confidentialité des données, un enjeu majeur pour ce type d'objet connecté. L'adoption d'un Kata Friend vous coûtera environ 700€. Mais l'addition ne s'arrête pas là. Loin de là. L'accès à l'intelligence et aux fonctionnalités logicielles est conditionné par un modèle par abonnement. Une tentative de s'épargner les frais de vétérinaire qui, au final, se transforme en une rente technologique. Les plans tarifaires proposés par SwitchBot sont clairs et segmentent l'expérience : Grâce à leur grand modèle de langage (LLM) intégré, Noa et Niko peuvent interagir, apprendre et répondre en temps réel sans avoir besoin d'une connexion Wi-Fi permanente. Ils utilisent leurs caméras et capteurs pour identifier les différents membres du foyer et adapter leurs interactions en fonction de la personne qu'ils ont en face d'eux. Oui, le lancement des Kata Friends s'inscrit dans une offensive IA plus large. La société a également dévoilé un Hub IA pour la maison connectée et un cadre artistique E-Ink capable de générer des œuvres via l'IA. La discussion est réservée aux membres GNT Commencez par créer un compte ou vous identifier Copyright © 2001-2026 GNT Media, tous droits réservés

Suggestions or feedback? Previous image Next image Imagine working at a warehouse or office sometime in the near future, and you’re asked to help a new trainee learn the basics of their job. The catch: It’s a robot. To teach them, you might want to play a game of “show and tell” — that is, physically showing how to do something a few different ways, while also explaining what you’re doing. Let’s say you asked the robot to place some coffee on your desk without disturbing you during a Zoom call. You’ll prefer that the robot doesn’t get too close to you and the laptop so that it doesn’t interrupt your meeting. To enable this behavior, the robot should be trained with data that clearly demonstrates the full task. Computer scientists have attempted to explain manipulation tasks to robots by recording lots of physical demonstrations or writing extensive directions. But if you don’t have both, the machine is likely to misunderstand what it needs to do.It’s laborious for humans to do all that showing and telling, so researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have automated the process of teaching a robot, while clarifying instructions automatically and using nearly five times less demonstration data. Their “Masked Inverse Reinforcement Learning” (Masked IRL) approach uses a large language model (LLM) to elaborate on ambiguous prompts based on the data collected from a user’s demo. Another LLM then narrows down which details an algorithm should incorporate into a motion plan, so that a robot can safely complete chores in homes, offices, and factories.“Our approach could come in handy when a human interacts with a robot but doesn’t want to spell out all the details of a task,” says MIT PhD student and CSAIL researcher Minyoung Hwang, who is a lead author on a paper presenting the project. “We’re minimizing human effort by enabling machines to get to the bottom of what users really want.” According to Hwang, Masked IRL can help robots safely maneuver in settings where there are elements a human might not describe in a prompt, but that are crucial nonetheless. For example, a machine grabbing you a snack from the kitchen may not know to avoid bumping into your laptop. Likewise, a factory robot placing items into different boxes must carefully navigate around shelves. To learn new tasks in these situations, Masked IRL uses the robot’s sensors to capture information about its surroundings. These components also log each movement of a kinesthetic demonstration — a training approach where a human physically moves a robot to do a specific action. It’s sort of like being the machine’s physical therapist, bending joints in a particular direction to show a robot how to grab, move, and place objects. MIT’s system then calls on an LLM to compare this sequence of motions (called a trajectory) to the shortest possible path. The model also elaborates on what might be unclear in a prompt, turning a request like “stay close” into “stay close to the surface of the table.” Using the trajectory comparison and clarified directions, the LLM begins to understand why the motions it was trained on are important to the task. A second LLM then evaluates details of the environment, such as the position of obstacles and the shape of the robot’s target object. During this process, it “masks” (in other words, ignores) the elements it deems irrelevant to the task at hand, scoring each one as either a “1” (important) or “0” (not so much). For example, whether or not a user was leaning on a table during a demonstration would be a “0,” making it irrelevant. Any detail considered a “1” is incorporated into the final action plan by an algorithm.These masks gave Masked IRL a key advantage over comparable baselines in both 3D and real-world demos because it taught a robot which information to prioritize. Thanks to the researchers’ system, virtual and real robots alike were able to skillfully maneuver objects around obstacles, such as moving a coffee mug around a laptop to different spots on a table. In these tasks, Masked IRL correctly identified users’ preferences, which they didn’t explicitly state in their prompts, up to 15 percent more often than comparable baselines.During simulation experiments, CSAIL researchers also found that Masked IRL was a fast learner. It required fewer demos to understand how to move the mug than its baselines. They also found that the robots performed better when an LLM cleared up instructions, instead of having the machine try to follow a vague request.This more focused approach also translated well to a real robotic arm, executing prompts the system hadn’t seen during its training phase. After being trained on 50 kinesthetic demonstrations, the robot carefully moved a cup toward a human while avoiding colliding with a user’s computer — an obstacle it learned to avoid by elaborating on a more general request to “stay away.” It also wiped a table down while “staying close” to it, and handed a user a bag of chips while “staying away” from both a human and a table. Masked IRL senses and explains what users leave unsaid, but soon, it might “see” it too. CSAIL researchers plan to make their approach more dynamic by equipping it with cameras, allowing a robot to take images of its surroundings. Then it could highlight and focus on specific elements nearby. For example, if you asked the machine to pick up a toy, it might see some bananas nearby and ignore them before handling its target object. Hwang wrote the paper with three CSAIL colleagues: PhD student Alexandra Forsey-Smerek ’20, SM ’22; postdoc Nathaniel Dennler; and MIT Assistant Professor Andreea Bobu, who is a member of the Department of Aeronautics and Astronautics and CSAIL. Their work was supported, in part, by the Tata Group via the MIT Generative AI Impact Consortium Award, and the Department of Defense. They’ll present the project at the 2026 IEEE International Conference on Robotics and Automation in June. Previous item Next item Read full story → Read full story → Read full story → Read full story → Read full story → Read full story → This website is managed by the MIT News Office, part of the Institute Office of Communications. Massachusetts Institute of Technology77 Massachusetts Avenue, Cambridge, MA, USA

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 U1 features 88 DOF and an emotion-aware LLM that recognizes 20+ emotional states. Chinese firm UBTech has introduced the UWORLD U1 Series, which it describes as the world’s first full-size ultra-bionic humanoid robots designed for mass production. Announced on June 30 at UBTech’s 2026 Global Launch Event in Shenzhen, the platform combines proprietary biomimetic skin, embodied AI hardware, an operating system, emotion-driven large language models (LLMs), and system-level manufacturing. UBTech says the integrated architecture is designed to overcome key barriers to deploying humanoid robots at scale in real-world environments. At the event, the company outlined a three-stage vision for human-robot collaboration, progressing from hazardous work to everyday assistance and ultimately seamless interaction between people and intelligent robots. The UWORLD U1 Series is built on UBTECH’s proprietary end-to-end technology stack, integrating biomimetic skin, embodied intelligence hardware, an operating system, emotion-driven large language models (LLMs), and system-level manufacturing to improve the real-world deployment of humanoid robots. The full-size humanoid features 88 degrees of freedom and a dual-pivot biomimetic cervical spine that enables it to reproduce up to 90 percent of fundamental human movements. It is powered by what UBTech describes as the world’s first emotion-aware LLM for long-term companionship, capable of identifying more than 20 fine-grained emotional states with over 90 percent accuracy. Its biomimetic fast-and-slow brain architecture combines rapid intuitive responses with deep reasoning. The system delivers reactions in about 500 milliseconds while supporting complex decision-making through AI models with hundreds of billions of parameters. A proprietary facial expression actuation system synchronizes speech and lip movements with less than 20 milliseconds of latency, producing more natural communication. The platform also incorporates Agent Memory OS, a cross-temporal memory framework that enables persistent long-term interactions. A proactive care engine continuously interprets environmental context, allowing wake-word-free communication and context-aware responses. UBTech has also implemented a three-layer privacy architecture based on local-first processing, minimal cloud reliance, and user-controlled hardware protections. Together, these technologies are designed to support advanced cognitive capabilities, natural human-robot interaction, persistent memory, and secure data handling across consumer and commercial humanoid robot applications. The company also outlined its long-term vision for human-robot collaboration, describing a three-stage evolution. The first focuses on deploying robots for hazardous and repetitive tasks, followed by expanding their role into everyday life through companionship and service applications. The final stage envisions increasingly seamless interaction between humans and intelligent robots across daily life. Along with the UWORLD, UBTech also introduced the Human-Robot Companionship Initiative, a program aimed at using humanoid robots to provide long-term emotional and psychological support for vulnerable groups. The company cited demographic trends in China, noting that more than 90 million adults live alone and 118 million older adults are classified as empty-nest seniors. It also said an estimated 10 percent to 20 percent of people living alone meet the clinical criteria for mental health disorders, highlighting the need for accessible companionship and care. As part of the initiative, the company expects to donate 100 customized U1 Series humanoid robots in 2026. Each unit will incorporate 3D facial reconstruction and voiceprint-based identity replication technologies to recreate designated individuals for personalized interaction. According to UBTech, the robots will also integrate emotion-driven interaction models, dedicated long-term memory systems, and multimodal situational awareness, enabling them to recognize users, retain memories across interactions, understand environmental context, and deliver more personalized companionship and psychological support over extended periods. 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

Written by Cheng Zi Published on 7 Jul 2026 5 mins read On June 30, UBTech Robotics hosted its 2026 global launch event in Shenzhen, where it unveiled the UWorld U1 Series of full-size humanoid robots. The lineup includes three models: the U1 Lite semi-torso edition, the high-performance full-body U1 Pro, and the high-dynamic full-body U1 Ultra. Prices range between RMB 119,800–990,000 (USD 17,613–145,549.7). UBTech said cumulative orders for the UWorld U1 Series had surpassed 13,361 units as of the day of the launch event. The significance of that number may lie less in sales volume itself than in the structural shift it points to: humanoid robots are moving out of factories and into homes. The key variables in industry competition are also changing, from motion control precision and payload capacity to a more basic question: how can robots enter everyday life and build long-term emotional connections with users? A research report by Guotai Junan Securities estimates that potential demand for companion robots among China’s elderly population stands at about RMB 420 billion (USD 61.7 billion), while demand among younger consumers is around RMB 500 billion (USD 73.5 billion), putting the combined potential market at close to RMB 1 trillion (USD 147 billion). The same report estimates that China’s overall smart companion robot market reached RMB 12.86 billion (USD 1.9 billion) in 2025, up 24.3% year-on-year. At the event, James Zhou, founder, chairman, and CEO of UBTech, said the company’s strategy of achieving “symbiosis” between humans and robots would unfold in three stages. Robots would first replace high-risk, repetitive labor, then enter everyday life scenarios and provide companionship services. Eventually, Zhou said, humans and robots would become deeply integrated, with the boundaries between them fading. Zhou also suggested that, once robots are widely adopted, human income may come from three categories: basic security, creative earnings, and “dividends” from machine operations. That view aligns with UBTech’s strategy of moving from industrial applications to commercial uses and then to consumer markets. On the industrial side, the Walker S series entered mass production and began deliveries in 2025. On the consumer side, the launch of UWorld U1 gives UBTech a product line stretching from business-to-business industrial robots to business-to-consumer companion robots. In hardware terms, the UWorld U1 series is equipped with 88 bionic joints. Built on UBTech’s own dual-pivot biomimetic cervical spine, the robot can replicate 90% of everyday human body movements, according to the company. Those movements include more than 300 composite micro-expressions across four major categories, humanlike neck mobility, and posture retention. Even when the robot is powered off, UBTech said its full-time self-locking technology can maintain head posture, reducing the rigid feel often associated with mechanical movement. Its built-in multidimensional flexible electronic skin allows the robot to sense the force and location of touch, enabling it to respond physically to hugs and leaning gestures. A full set of bionic hardware addresses the basic question of whether a robot can resemble a person. But companionship in the home requires more than physical resemblance. The robot also has to understand people and interpret them. UBTech presents its emotion-aware large language model (LLM) as UWorld U1’s core capability. Michael Tam, chief brand officer of UBTech and president of its robotics consumer innovation division, said the model can recognize more than 20 fine-grained emotional states with accuracy exceeding 90%. In its architecture, UWorld draws on cognitive neuroscience to build a dual cognition design. One half, the “faster,” enables intuitive responses at the 500-millisecond level, while the “slower” counterpart supports deeper reasoning using hundreds of billions of parameters. Together, the two systems are designed to help the robot maintain real-time interaction while also tracking changes in a user’s emotions over the course of a long-term relationship. Its cross-temporal memory system, Agent Memory OS, is designed to build a “digital life” system that allows the robot to continuously record, understand, and respond to changes in the user’s condition throughout the product lifecycle. Its proactive care engine operates through environmental sensing, meaning users generally do not need to deliberately wake the robot. Instead, UBTech said the robot can independently determine when to step in. At the level of biomimetic interaction, UWorld uses a self-developed controller to keep the delay between speech and lip movement within 20 milliseconds, reducing the sense of distance in human-robot interaction. UBTech also addressed data security, one of the main concerns around home companion robots. The company said the UWorld U1 series prioritizes local processing of user data and avoids cloud uploads unless necessary. Users can view, export, and delete their data at any time, according to the company. At the launch event, UBTech also introduced a donation initiative. It plans to donate customized bionic robots each year to groups including empty-nest seniors, families that have lost their only child, and families of people officially recognized as martyrs. The company plans to donate 100 units in 2026. Through 3D facial reconstruction and voiceprint-based identity replication technology, the robots can recreate a designated likeness, combining an emotion-interaction large model with dedicated long-term memory to provide psychological companionship. From an industry perspective, humanoid robot development over the past several years has centered on mobility and operational precision: enabling robots to stand steadily, walk, grasp, and carry. These capabilities have direct value in industrial settings. But once robots enter homes, the evaluation system changes. Users may care less about how much cargo a robot can move than whether it feels natural, responsive, and trustworthy. Macro data suggests that shift is still at an early stage. According to IDC, global humanoid robot shipments totaled about 18,000 units in 2025, with less than 0.8% going into private homes. More than 90% of products remained concentrated in industrial settings. Morgan Stanley predicts that China’s humanoid robot shipments in 2026 could reach 50,000 units, triple its estimate at the start of the year. It expects the market to reach USD 2 billion this year and climb to USD 15 billion by 2030. Beyond technical feasibility, UBTech is trying to make the case that consumer acceptance is beginning to form. The end-to-end proprietary technology stack it has built over the past decade, spanning biomimetic skin, embodied intelligence hardware, operating systems, emotion-aware LLMs, and system-level mass production, gives it a foundation for mass production and delivery. But humanoid robots still face practical challenges before they can become common in the home. It remains to be tested whether emotional interaction in long-term companionship scenarios can be sustained without becoming a novelty; how products can use over-the-air updates throughout their lifecycles to keep the interaction experience evolving; and how data security and ethical boundaries should be defined after large-scale deployment. KrASIA features translated and adapted content that was originally published by 36Kr. This article was written by Huang Nan for 36Kr. Note: RMB figures are converted to USD at rates of RMB 6.80 = USD 1 based on estimates as of July 7, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates. Loading... Subscribe to our newsletters KrASIA A digital media company reporting on China's tech and business pulse.

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ARY NEWS brings you 24/7 Live Streaming, Headlines, Bulletins, Talk Shows, Infotainment, and much more. Tokyo: Japan plans to develop a homegrown artificial intelligence model and have 10 million AI-equipped robots operating in more than a dozen sectors by 2040, the government said. The country will reportedly invest around $6 billion in the homemade AI model, which will be developed by Noetra, a consortium of firms including SoftBank and Sony. Countries around the world are seeking to develop sovereign AI models to reduce a potentially dangerous over-reliance on technology from the United States and China. Media reports said that the government would provide up to one trillion yen ($6.1 billion) over the next five years, depending on the results of the initiative. The number of companies investing in Noetra is expected to increase to 44 including those in the automotive, electronics and other manufacturing sectors as well as finance and logistics, the Nikkei business daily reported. Noetra will focus in particular on physical AI, with the government also announcing on Tuesday a revised version of its AI robotics strategy. Instead of just interacting with users via software on a screen, physical AI is about the deployment of artificial intelligence in real-world settings — such as self-driving cars, factory robots or even android butlers. Despite massive investments and big plans for AI robots, their application and performance in real-life settings remain limited. “This strategy sets a target of approximately 10 million robots to be deployed by 2040 and, with the addition of the restaurant, food manufacturing and medical sectors, will vigorously promote social implementation across a total of 18 fields,” Industry Minister Ryosei Akazawa told reporters. “We will build and grow data infrastructure for physical AI and robots that capitalise on Japan’s strengths,” he said. With its ageing and shrinking population, Japan is also hoping that robots can help plug gaps in the workforce. Last month Japan announced a 14-year growth strategy targeting public and private investment of 370 trillion yen ($2.3 trillion) in 17 sectors including physical AI, chips, quantum technology and nuclear fusion. Neighbouring South Korea this week announced record public-private investments worth hundreds of billions of dollars into AI data centres and chipmaking over several years. ARY NEWS brings you 24/7 Live Streaming, Headlines, Bulletins, Talk Shows, Infotainment, and much more. Watch minute-by-minute updates of current affairs and happenings from Pakistan and all around the world!

After the global announcement, Xiaomi held a launch event today to introduce the Xiaomi 17T and Xiaomi 17T Pro in China. They start at 2,999 yuan (~ $445) and 3,999 yuan (~ $590), respectively, and don’t differ much in specs from the global counterpart. But the event wasn’t just about phones. Xiaomi also gave its next-generation humanoid robot a brief moment on stage to show some tricks. During the demonstration, the robot picked up the new Xiaomi 17T Pro using its bionic hand, adjusted the zoom using the volume buttons, and took a photo. Below is the shot he took. It was a short appearance, but it gave the audience a direct look at where Xiaomi’s robotics work currently stands in terms of physical dexterity and device control. The company has been showing off its humanoid robot progress in stages every once in a while. We have covered earlier milestones here, including Xiaomi’s plans to deploy CyberOne in its own manufacturing lines and Lei Jun’s stated goal of getting humanoid robots into Xiaomi factories at scale within five years. The industrial progress behind the scenes has been steady. Earlier this year, Xiaomi confirmed that its robots had been running three-hour continuous shifts at a self-tapping nut installation station in a car factory, hitting a 90.2% success rate for simultaneous bilateral installation while keeping up with the line’s 76-second cycle time requirement. On the hardware side, Xiaomi announced that it had reworked the bionic hand used on its robots. The new version is 60% smaller in volume, now matching the size of a human worker’s hand. It also has 64% more degrees of freedom, a full-palm tactile sensor with 8,200 square millimetres of coverage, a gripping cycle life of over 150,000 operations, and a bionic sweat gland system for heat management during extended use. Lei Jun also recently posted on Weibo that Xiaomi’s robotics team took first place in two separate international competitions, outpacing the runner-up teams by a wide margin. He reiterated that the focus of Xiaomi’s robotics work is on getting robots to carry out practical tasks in real-world environments, not just controlled settings. For more daily updates, please visit our News Section. Stay ahead in tech! Join our Telegram community and sign up for our daily newsletter of top stories! 💡

Oops, something went wrong Benzinga and Yahoo Finance LLC may earn commission or revenue on some items through the links below. Shares of Churchill Capital Corp XI are trading higher Monday morning following last week's announcement of a definitive business combination agreement with Agility Robotics. The transaction values the humanoid robotics and physical AI company at a pre-money equity value of $2.5 billion. Here's what investors need to know. Don't Miss: The Average Family's Finances Are More Complicated Than Ever. These Tools Aim To Make Them Easier To Manage. Think Your 'Safe' Stocks Protect You? You're Ignoring the Real Growth Triggers — Here's What to Add Now The merger is expected to provide Agility with over $620 million in gross proceeds. This includes $420 million of cash held in Churchill XI's trust account and a $200 million common stock PIPE priced at $10 per share. The PIPE is led by Foxconn, alongside existing and new institutional investors. Agility Robotics produces Digit, a commercially deployed humanoid robot designed for manufacturing and logistics environments. The company has secured over $300 million in multi-year contracted orders for its upcoming Digit v5. Agility's strategic investors include Amazon, NVIDIA and SoftBank Vision Fund 2. Trending: Caught With Nothing Saved for Retirement? These 5 Game‑Changing Tips Could Still Save You In a recent interview, Agility CEO Peggy Johnson highlighted the company's robots-as-a-service business model and first-mover advantage. Furthermore, an amended Schedule 13D filing indicates Churchill Sponsor XI LLC holds a 25.7% stake in CCXI. All existing Agility shareholders are rolling their equity into the combined company and will be subject to a 180-day lock-up. Upon closing, the combined entity will list under the ticker "AGLT". Image: Shutterstock Read Next: Think you're saving enough for your kids? You might be dangerously off — see why Still Learning the Market? These 50 Must-Know Terms Can Help You Catch Up Fast Building a resilient portfolio means thinking beyond a single asset or market trend. Economic cycles shift, sectors rise and fall, and no one investment performs well in every environment. That's why many investors look to diversify with platforms that provide access to real estate, fixed-income opportunities, precious metals, and even self-directed retirement accounts. By spreading exposure across multiple asset classes, it becomes easier to manage risk, capture steady returns, and create long-term wealth that isn't tied to the fortunes of just one company or industry. Backed by Jeff Bezos, Arrived Homes makes real estate investing accessible with a low barrier to entry. Investors can buy fractional shares of single-family rentals and vacation homes starting with as little as $100. This allows everyday investors to diversify into real estate, collect rental income, and build long-term wealth without needing to manage properties directly. The rapid adoption of artificial intelligence is creating significant demand for data centers, power, and compute infrastructure. BluSky AI is building modular AI data centers designed to support next-generation AI workloads while aiming to reduce deployment timelines compared to traditional facilities. For investors looking beyond AI software and applications, the company offers exposure to the infrastructure layer that makes artificial intelligence possible. Residential real estate has historically provided investors with income potential and long-term appreciation, but direct ownership can be expensive and time-consuming. ARK7 enables investors to buy fractional shares of rental properties, offering access to potential rental income and real estate exposure without property management responsibilities. By lowering the barrier to entry, the platform gives investors another way to diversify beyond traditional stocks and bonds. Immersed is building technology for the future of work through spatial computing. Known for its AR/VR productivity platform that enables users to work across multiple virtual screens, the company has grown to more than 1.5 million users worldwide. Immersed is also developing Visor, a lightweight headset designed specifically for professional productivity, positioning the company at the intersection of remote work, extended reality (XR), and next-generation computing. Robotics and automation are becoming increasingly important tools for businesses facing labor shortages and rising operating costs. Miso Robotics develops AI-powered kitchen technology that is already being deployed in restaurant environments, with products designed to help operators improve efficiency and streamline operations. As artificial intelligence expands beyond software and into real-world applications, the company is positioning itself at the intersection of robotics, automation and the future of food service. Fine wine and rare whiskey have historically moved independently of the stock market, making them a compelling alternative asset. Vinovest manages authenticated, insured portfolios of investment-grade wine and whiskey starting at $5,000 — sourcing, storage, and insurance all handled for you. Farmland has historically held its value through market volatility and delivered returns uncorrelated to stocks and bonds. For accredited investors, FarmTogether offers direct access to high-quality U.S. farmland starting at $15,000 — fully managed, with no landlord headaches. For accredited investors looking beyond stocks and bonds, EquityMultiple provides access to vetted commercial real estate deals starting at $5,000, with only ~5% of opportunities passing their due diligence process. Private real estate and private credit can add income and stability to a stock-heavy portfolio. Fundrise offers access to diversified private real estate and credit strategies through an easy-to-use platform, with professionally managed portfolios designed to generate passive income and long-term growth. American Hartford Gold is a precious metals dealer that helps clients buy physical gold and silver coins and bars, either for direct delivery or within self-directed precious metals IRAs. The company's services include gold and silver IRAs, IRA rollovers, and home delivery of bullion, giving investors a way to use tangible metals to diversify portfolios and seek protection against inflation and market volatility. Mode Mobile is changing the way people interact with their phones by letting users earn money from the same apps and activities they already use every day. Instead of platforms keeping all the advertising revenue, Mode Mobile shares a portion back with users who engage with content, play games, and scroll on their devices. Named one of Deloitte's fastest-growing software companies in North America, the company has built a large beta user base and is scaling a model that turns everyday smartphone usage into a potential income stream. © 2026 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

Από το βελτιωμένο περπάτημα στον χορό. Τα βίντεο που αφορούν την εξέλιξη του Optimus συνεχίζονται, αυτή τη φορά με τον Elon Musk να δημοσιεύει ένα ακόμη, που εμφανίζει το ανθρωποειδές ρομπότ της Tesla, σε χορευτικές φιγούρες που εντυπωσιάζουν με την ευελιξία και τον συγχρονισμό τους. Το βίντεο σηματοδοτεί την πρόοδο που έχει σημειώσει η Tesla στον τομέα της ρομποτικής, με το Optimus να αποκτά όλο και πιο εκλεπτυσμένες κινήσεις και πλέον, να προχωρά στα πρώτα βήματα της παραγωγής. Το 2022, η Tesla παρουσίασε τα πρώτα ημι-λειτουργικά Optimus, με ένα από αυτά να περπατά στη σκηνή και ένα άλλο να εκτελεί βασικές κινήσεις χεριών. Εκείνη την εποχή, πολλοί επικριτές σημείωσαν την εξάρτηση του ρομπότ από τον τηλεχειρισμό. Διαβάστε ολόκληρο το άρθρο στο topgeargreece.gr Εμείς μαζέψαμε αυτά τα 7 ιδιαίτερα αυτοκίνητα ("ιδιαίτερα" όπως λέμε σε φίλο που μόλις έκανε χάλια θεατρική παράσταση ότι ήταν "ενδιαφέρουσα") και εσείς κάντε ό,τι νομίζετε. Οδηγούσε σαν διάβολος αλλά ζούσε σαν άγιος: Γνωρίστε τον Ab Jenkins, τον άνθρωπο που κατέρριψε πάνω 500 ρεκόρ ταχύτητας Αυτή η Continental GT Supersports της Mulliner θυμίζει περισσότερο δημιουργία ανεξάρτητου βελτιωτή παρά της ίδιας της Bentley και θα κάνει την πρώτη του εμφάνιση στο Goodwood Festival of Speed 2026. Στο περιθώριο της παρουσίασης του Skoda Peaq είχαμε την ευκαιρία να συνομιλήσουμε με τον επικεφαλής εξέλιξης των αυτοκινήτων της Skoda, Johannes Neft και δεν ασχοληθήκαμε μόνο με το Peaq. Εγγύηση εφ' όρου ζωής δίνει μεγάλη κινεζική φίρμα για τις μπαταρίες στα αμιγώς ηλεκτρικά, PHEV και υβριδικά της αυτοκίνητα -την ίδια εγγύηση έχουν οι ηλεκτροκινητήρες της και οι μονάδες ελέγχου. Η αυξανόμενη οικονομική πίεση επαναφέρει στο προσκήνιο τη συζήτηση για το ενδεχόμενο η Volkswagen να αποχωριστεί μερικά από τα κορυφαία ονόματα του χαρτοφυλακίου της. Το TAG Heuer Formula 1 Automatic Chronograph x Gulf αποτυπώνει την ένταση των αγώνων σε ένα μηχανικό χρονογράφο, εμπνευσμένο από τα εμβληματικά χρώματα της Gulf. O Elon Musk είναι ο πρώτος τρισεκατομμυριούχος στον κόσμο και μπορεί να αγοράσει ότι θέλει ενώ θα έχει και ρέστα. Μια απολύτως λογική εμφάνισή του θα ήταν με τον θεό Δία στο τιμόνι να στέκεται ανάποδα στην οροφή της Ακρόπολης. Ναι, αυτό μπορεί να το κάνει σταματημένο! Υποδεχθείτε το απόλυτο αυτοκίνητο παραγωγής... Μόλις 1,5 χρόνο μετά τις πρώτες δοκιμές ενός ηλεκτρικού αεροσκάφους κάθετης απογείωσης και προσγείωσης, η Toyota κάνει κοινοπραξία με γνωστή εταιρεία eVTOL. Μια νέα συνεργασία του incharge με κορυφαία ελληνική εταιρεία στον κλάδο της φέρνει στην Ελλάδα τα πρώτα αμιγώς ηλεκτρικά δρομολόγια μεταφοράς μεταλλευμάτων. To Linktour Alumi είναι ένα νέα νέο ηλεκτρικό διθέσιο αυτοκίνητο που μόλις ήρθε στην ελληνική αγορά με τιμές που ξεκινούν από 8.700 ευρώ. Δεν πρόκειται για τυπογραφικό λάθος. Η αναβίωση ενός από τα πιο σημαντικά μίνι αυτοκίνητα της Ευρώπης είναι ηλεκτρική και με τιμή στην Ελλάδα χαμηλότερη από θερμικό μοντέλο. Δια χειρός του CEO της Citroen, Xavier Chardon, ο Όμιλος Συγγελίδη και οι διανομείς της Citroen στην Ελλάδα παρέλαβαν το κορυφαίο βραβείο στην Ευρώπη. Στη Ρώμη και στους δρόμους του Βατικανού, το μικρό ηλεκτρικό της Fiat έδειξε γιατί η μικροκινητικότητα μπορεί να είναι πρακτική, έξυπνη και γεμάτη χαρακτήρα. Πότε αναμένεται στην Ελλάδα και σε τι... Μελέτη του Euro NCAP δείχνει πως οι ελλείψεις ασφαλείας σε συγκεκριμένα οχήματα ευθύνονται για 400 θανάτους κάθε χρόνο σε όλη την Ευρώπη. Ταυτότητα Επικοινωνία Όροι χρήσης Θέσεις εργασίας Οικονομικά στοιχεία © Capital.gr 2026

Tesla expands Robotaxi to Florida, marking its third state for autonomy Elon Musk outlines Tesla Optimus production expectations Tesla brings Model Y L ‘Launch Series’ to the U.S. at $61,990 Tesla just told us twice that Model Y L is coming to the U.S. Tesla is using vehicle microphones to improve build quality: here’s how Tesla expands Robotaxi to Florida, marking its third state for autonomy Elon Musk outlines Tesla Optimus production expectations Tesla brings Model Y L ‘Launch Series’ to the U.S. at $61,990 Tesla just told us twice that Model Y L is coming to the U.S. Tesla is using vehicle microphones to improve build quality: here’s how SpaceX gets initial stock coverage from Tesla’s biggest bull Tesla Phone? Not quite, but close: analyst SpaceX’s newest Starmind will make earth data centers obsolete SpaceX makes $20 billion move to optimize its balance sheet SpaceX confirms third massive compute deal at Colossus data center Elon Musk outlines Tesla Optimus production expectations Tesla Optimus project fires up as Musk sees production line progress Tesla Phone? Not quite, but close: analyst SpaceX’s newest Starmind will make earth data centers obsolete Tesla finally clarifies fatal Texas crash, confirms driver manually overrode acceleration Published on By Tesla CEO Elon Musk has tempered expectations for the company’s humanoid robot Optimus, emphasizing that initial production will ramp up slowly despite recent progress on the manufacturing line. In a July 1 reply on X, Musk responded to optimistic community speculation by stating, “No, Optimus production will be extremely slow at first, as everything is new. This is not like making a car.” No, Optimus production will be extremely slow at first, as everything is new. This is not like making a car. — Elon Musk (@elonmusk) July 1, 2026 The comment came in response to a post theorizing that Tesla had accelerated Optimus V3 development and might soon unveil an impressive demonstration with multiple units already in meaningful production. Musk’s clarification highlights the fundamental differences between scaling a novel humanoid robot and Tesla’s established automotive operations, which benefit from over a century of refined supply chains, tooling, and processes.Advertisement Recent updates show tangible advancement. Musk shared a photo of himself walking the Optimus production line at Fremont, where Tesla is converting former Model S/X manufacturing space. According to Q1 2026 earnings commentary, limited production is slated to begin in late July or August 2026 on this converted line. Tesla Optimus project fires up as Musk sees production line progress Musk previously noted that Optimus features roughly 10,000 unique parts, making early output rates “literally impossible to predict” and describing them as “quite slow.” A larger dedicated factory at Giga Texas is under construction, targeting higher-volume production around summer 2027 with long-term annual capacity potentially reaching millions of units. Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement No, Optimus production will be extremely slow at first, as everything is new. This is not like making a car. — Elon Musk (@elonmusk) July 1, 2026 — Elon Musk (@elonmusk) July 1, 2026 The comment came in response to a post theorizing that Tesla had accelerated Optimus V3 development and might soon unveil an impressive demonstration with multiple units already in meaningful production. Musk’s clarification highlights the fundamental differences between scaling a novel humanoid robot and Tesla’s established automotive operations, which benefit from over a century of refined supply chains, tooling, and processes.Advertisement Recent updates show tangible advancement. Musk shared a photo of himself walking the Optimus production line at Fremont, where Tesla is converting former Model S/X manufacturing space. According to Q1 2026 earnings commentary, limited production is slated to begin in late July or August 2026 on this converted line. Tesla Optimus project fires up as Musk sees production line progress Musk previously noted that Optimus features roughly 10,000 unique parts, making early output rates “literally impossible to predict” and describing them as “quite slow.” A larger dedicated factory at Giga Texas is under construction, targeting higher-volume production around summer 2027 with long-term annual capacity potentially reaching millions of units. Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement The comment came in response to a post theorizing that Tesla had accelerated Optimus V3 development and might soon unveil an impressive demonstration with multiple units already in meaningful production. Musk’s clarification highlights the fundamental differences between scaling a novel humanoid robot and Tesla’s established automotive operations, which benefit from over a century of refined supply chains, tooling, and processes.Advertisement Recent updates show tangible advancement. Musk shared a photo of himself walking the Optimus production line at Fremont, where Tesla is converting former Model S/X manufacturing space. According to Q1 2026 earnings commentary, limited production is slated to begin in late July or August 2026 on this converted line. Tesla Optimus project fires up as Musk sees production line progress Musk previously noted that Optimus features roughly 10,000 unique parts, making early output rates “literally impossible to predict” and describing them as “quite slow.” A larger dedicated factory at Giga Texas is under construction, targeting higher-volume production around summer 2027 with long-term annual capacity potentially reaching millions of units. Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Recent updates show tangible advancement. Musk shared a photo of himself walking the Optimus production line at Fremont, where Tesla is converting former Model S/X manufacturing space. According to Q1 2026 earnings commentary, limited production is slated to begin in late July or August 2026 on this converted line. Tesla Optimus project fires up as Musk sees production line progress Musk previously noted that Optimus features roughly 10,000 unique parts, making early output rates “literally impossible to predict” and describing them as “quite slow.” A larger dedicated factory at Giga Texas is under construction, targeting higher-volume production around summer 2027 with long-term annual capacity potentially reaching millions of units. Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Tesla Optimus project fires up as Musk sees production line progress Musk previously noted that Optimus features roughly 10,000 unique parts, making early output rates “literally impossible to predict” and describing them as “quite slow.” A larger dedicated factory at Giga Texas is under construction, targeting higher-volume production around summer 2027 with long-term annual capacity potentially reaching millions of units. Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Musk previously noted that Optimus features roughly 10,000 unique parts, making early output rates “literally impossible to predict” and describing them as “quite slow.” A larger dedicated factory at Giga Texas is under construction, targeting higher-volume production around summer 2027 with long-term annual capacity potentially reaching millions of units. Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Some experts point out that pioneering humanoid robotics demands inventing new automation techniques, actuator supply chains, and quality-control standards in real time. Unlike vehicles, where components and assembly methods are mature, every element of Optimus—from dexterous hands to AI-integrated movement—requires fresh engineering solutions. Early units are expected to handle simple factory tasks before expanding to more complex roles.Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement This cautious approach aligns with Tesla’s history of under-promising and over-delivering on complex technologies. While enthusiasts hoped for rapid deployment, Musk’s message underscores a deliberate strategy: prioritize reliability and iterative improvement over rushed volume. Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Analysts suggest the S-curve ramp typical of new manufacturing will eventually accelerate once foundational issues are resolved, positioning Optimus as a potential trillion-dollar product line. Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Musk has long envisioned Optimus transforming labor markets, assisting in homes, factories, and hazardous environments. By setting realistic timelines, Tesla aims to build sustainable momentum rather than risk disappointment. As the Fremont line comes online this summer, investors and fans will watch closely for the first production metrics and capability demonstrations. Advertisement Joey has been a journalist covering electric mobility at TESLARATI since August 2019. In his spare time, Joey is playing golf, watching MMA, or cheering on any of his favorite sports teams, including the Baltimore Ravens and Orioles, Miami Heat, Washington Capitals, and Penn State Nittany Lions. You can get in touch with joey at joey@teslarati.com. He is also on X @KlenderJoey. If you're looking for great Tesla accessories, check out shop.teslarati.com Published on By Tesla CEO Elon Musk posted a photo of himself standing with the Optimus production team inside Tesla’s Fremont factory, arms crossed amid workers in hard hats and safety vests. The image captures a pivotal industrial shift: the same facility space once dedicated to building Tesla’s flagship Model S sedan and Model X SUV is now home to the company’s humanoid robot manufacturing line. Walking the Optimus production line in Fremont pic.twitter.com/ABS0tuRibW — Elon Musk (@elonmusk) July 1, 2026 Tesla’s Fremont Factory, acquired in 2010 from the former NUMMI joint venture between Toyota and GM, has been the company’s original U.S. manufacturing hub since Model S production began in 2012.Advertisement The Model X followed soon thereafter. These premium vehicles offered lower annual volumes, recently around 30,000 combined, compared to the high-volume Model 3 and Model Y lines that continue around the site. Over their combined run, the S and X accounted for roughly 610,000 units. In late January 2026, during Tesla’s Q4 2025 earnings call, Elon Musk announced the end of Model S and Model X production in Q2 2026. The final vehicles rolled off the line in early May. Rather than retooling for another vehicle, Tesla chose to convert the dedicated S/X assembly area into a dedicated Optimus Gen 3 production line. Model 3 and Y manufacturing remains unaffected. Tesla’s official Fremont Factory page now lists Optimus alongside the 3 and Y as core products. The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Walking the Optimus production line in Fremont pic.twitter.com/ABS0tuRibW — Elon Musk (@elonmusk) July 1, 2026 — Elon Musk (@elonmusk) July 1, 2026 Tesla’s Fremont Factory, acquired in 2010 from the former NUMMI joint venture between Toyota and GM, has been the company’s original U.S. manufacturing hub since Model S production began in 2012.Advertisement The Model X followed soon thereafter. These premium vehicles offered lower annual volumes, recently around 30,000 combined, compared to the high-volume Model 3 and Model Y lines that continue around the site. Over their combined run, the S and X accounted for roughly 610,000 units. In late January 2026, during Tesla’s Q4 2025 earnings call, Elon Musk announced the end of Model S and Model X production in Q2 2026. The final vehicles rolled off the line in early May. Rather than retooling for another vehicle, Tesla chose to convert the dedicated S/X assembly area into a dedicated Optimus Gen 3 production line. Model 3 and Y manufacturing remains unaffected. Tesla’s official Fremont Factory page now lists Optimus alongside the 3 and Y as core products. The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Tesla’s Fremont Factory, acquired in 2010 from the former NUMMI joint venture between Toyota and GM, has been the company’s original U.S. manufacturing hub since Model S production began in 2012.Advertisement The Model X followed soon thereafter. These premium vehicles offered lower annual volumes, recently around 30,000 combined, compared to the high-volume Model 3 and Model Y lines that continue around the site. Over their combined run, the S and X accounted for roughly 610,000 units. In late January 2026, during Tesla’s Q4 2025 earnings call, Elon Musk announced the end of Model S and Model X production in Q2 2026. The final vehicles rolled off the line in early May. Rather than retooling for another vehicle, Tesla chose to convert the dedicated S/X assembly area into a dedicated Optimus Gen 3 production line. Model 3 and Y manufacturing remains unaffected. Tesla’s official Fremont Factory page now lists Optimus alongside the 3 and Y as core products. The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement The Model X followed soon thereafter. These premium vehicles offered lower annual volumes, recently around 30,000 combined, compared to the high-volume Model 3 and Model Y lines that continue around the site. Over their combined run, the S and X accounted for roughly 610,000 units. In late January 2026, during Tesla’s Q4 2025 earnings call, Elon Musk announced the end of Model S and Model X production in Q2 2026. The final vehicles rolled off the line in early May. Rather than retooling for another vehicle, Tesla chose to convert the dedicated S/X assembly area into a dedicated Optimus Gen 3 production line. Model 3 and Y manufacturing remains unaffected. Tesla’s official Fremont Factory page now lists Optimus alongside the 3 and Y as core products. The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement In late January 2026, during Tesla’s Q4 2025 earnings call, Elon Musk announced the end of Model S and Model X production in Q2 2026. The final vehicles rolled off the line in early May. Rather than retooling for another vehicle, Tesla chose to convert the dedicated S/X assembly area into a dedicated Optimus Gen 3 production line. Model 3 and Y manufacturing remains unaffected. Tesla’s official Fremont Factory page now lists Optimus alongside the 3 and Y as core products. The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Model 3 and Y manufacturing remains unaffected. Tesla’s official Fremont Factory page now lists Optimus alongside the 3 and Y as core products. The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement The conversion was executed with remarkable speed. After production stopped, crews dismantled the existing vehicle line and installed entirely new modular equipment—including lines sourced from Germany and dozens of sub-lines for actuators, batteries, and other components—in roughly four months.Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Musk described the timeline as “insanely fast,” noting it would be unprecedented for any other manufacturer. Initial Optimus output is expected to ramp slowly due to the robot’s roughly 10,000 unique parts and the brand-new production processes involved. The Fremont line targets an eventual capacity of 1 million Optimus units per year. Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Tesla isn’t joking about building Optimus at an industrial scale: Here we go Optimus Development Timeline August 19, 2021: Optimus (then called Tesla Bot) formally announced at Tesla’s first AI Day. A concept video showed a person in a suit demonstrating the vision for a general-purpose humanoid capable of dangerous, repetitive, or boring tasks using the same AI architecture as Full Self-Driving. 2022: Early prototypes displayed. At the second AI Day in September, semi-functional units demonstrated walking across a stage and basic arm movements 2023: September videos showed improved capabilities, including sorting colored blocks, precise limb awareness, and holding a Yoda pose. 2024-early 2025: Factory integration videos showed Optimus navigating workspaces and handling objects like battery cells. January 2026: Gen 3 mass-production activities began at Fremont, with reports of over 1,000 Gen 3 units already operating inside the factory for real-world learning and AI training April 2026: Musk confirms Optimus production on converted Fremont line would begin in late July or August 2026. The Gen 3 reveal, originally eyed for Q1, was pushed closer to production start. A second, much larger Optimus factory at Giga Texas is under construction, with volume production targeted for Summer 2027 and long-term capacity of 10 million units annually July 1, 2026: Musk’s on-site visit and team photo confirm the Optimus line is operational and the transition is actively progressing Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Tesla positions Optimus as potentially its largest project ever, leveraging vertical integration, AI expertise, and car-like manufacturing know-how to scale humanoid robots first for its own factories and later for broader industrial and consumer use. The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement The Fremont conversion serves as a critical proving ground for this ambitious new chapter in Tesla’s already-rich history. Advertisement Published on By For years, there have been images and videos across social media platforms that have reminded me of when I was a 15-year-old kid teased by “Xbox 720” videos on YouTube. These videos are of the supposed “Tesla Phone” that Elon Musk was secretly developing in between leading Tesla with its electric cars and SpaceX with its reusable rockets. Would you buy a Tesla phone ? pic.twitter.com/aaTwvvIJit — Tesla Owners Silicon Valley (@teslaownersSV) October 6, 2023 Although Musk has put those rumors to bed several times, it was never completely out of the realm that he could get involved in cell phones in some capacity. Think outside the box and more macro-level, though. Instead of reinventing the computer, Musk reinvented connectivity by developing Starlink with SpaceX.Advertisement It could be something similar, TD Cowen analyst Gregory Williams said in a note last week, where he hinted SpaceX could be gathering some steam to acquire T-Mobile. Williams said it would be the “clear choice” for SpaceX if it decided to go through with a network acquisition. He also suggested AT&T. The move would be possible through selling more of its own stock, which would help SpaceX raise the money to purchase T-Mobile, which would cost roughly $300 billion. It could be one of the moves SpaceX makes post-IPO in terms of an acquisition: it already acquired Cursor AI for $60 billion. Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement Would you buy a Tesla phone ? pic.twitter.com/aaTwvvIJit — Tesla Owners Silicon Valley (@teslaownersSV) October 6, 2023 — Tesla Owners Silicon Valley (@teslaownersSV) October 6, 2023 Although Musk has put those rumors to bed several times, it was never completely out of the realm that he could get involved in cell phones in some capacity. Think outside the box and more macro-level, though. Instead of reinventing the computer, Musk reinvented connectivity by developing Starlink with SpaceX.Advertisement It could be something similar, TD Cowen analyst Gregory Williams said in a note last week, where he hinted SpaceX could be gathering some steam to acquire T-Mobile. Williams said it would be the “clear choice” for SpaceX if it decided to go through with a network acquisition. He also suggested AT&T. The move would be possible through selling more of its own stock, which would help SpaceX raise the money to purchase T-Mobile, which would cost roughly $300 billion. It could be one of the moves SpaceX makes post-IPO in terms of an acquisition: it already acquired Cursor AI for $60 billion. Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement Although Musk has put those rumors to bed several times, it was never completely out of the realm that he could get involved in cell phones in some capacity. Think outside the box and more macro-level, though. Instead of reinventing the computer, Musk reinvented connectivity by developing Starlink with SpaceX.Advertisement It could be something similar, TD Cowen analyst Gregory Williams said in a note last week, where he hinted SpaceX could be gathering some steam to acquire T-Mobile. Williams said it would be the “clear choice” for SpaceX if it decided to go through with a network acquisition. He also suggested AT&T. The move would be possible through selling more of its own stock, which would help SpaceX raise the money to purchase T-Mobile, which would cost roughly $300 billion. It could be one of the moves SpaceX makes post-IPO in terms of an acquisition: it already acquired Cursor AI for $60 billion. Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement It could be something similar, TD Cowen analyst Gregory Williams said in a note last week, where he hinted SpaceX could be gathering some steam to acquire T-Mobile. Williams said it would be the “clear choice” for SpaceX if it decided to go through with a network acquisition. He also suggested AT&T. The move would be possible through selling more of its own stock, which would help SpaceX raise the money to purchase T-Mobile, which would cost roughly $300 billion. It could be one of the moves SpaceX makes post-IPO in terms of an acquisition: it already acquired Cursor AI for $60 billion. Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement Williams said it would be the “clear choice” for SpaceX if it decided to go through with a network acquisition. He also suggested AT&T. The move would be possible through selling more of its own stock, which would help SpaceX raise the money to purchase T-Mobile, which would cost roughly $300 billion. It could be one of the moves SpaceX makes post-IPO in terms of an acquisition: it already acquired Cursor AI for $60 billion. Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement The move would be possible through selling more of its own stock, which would help SpaceX raise the money to purchase T-Mobile, which would cost roughly $300 billion. It could be one of the moves SpaceX makes post-IPO in terms of an acquisition: it already acquired Cursor AI for $60 billion. Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement Other analysts, like Dan Ives of Wedbush, believe SpaceX and Tesla will eventually merge into one anyway, and that conglomeration could come as soon as this year, some have said.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement The implications of SpaceX purchasing T-Mobile are massive. A combined entity would create a truly ubiquitous network: T-Mobile’s terrestrial 5G towers and Starlink’s growing constellation of Direct-to-Cell satellites. This would essentially eliminate dead zones across the U.S. and potentially globally. SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement SpaceX would instantly become a full-scale facilities-based carrier with satellite differentiation; a huge advantage. This would pressure AT&T and Verizon heavily. There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement There are also concerns like a potential reduction in long-term competition, and of course, a deal of that size would face intense scrutiny from government agencies. The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement The strategic fit is compelling due to the existing Starlink–T-Mobile partnership and complementary technologies (space + terrestrial). It could create a dominant integrated communications player. However, the regulatory, financial, and execution hurdles are enormous — this remains highly speculative with no indication SpaceX is actively pursuing it right now.Advertisement Elon Musk confirmed Starmind as SpaceX’s AI satellite constellation name, targeting one million orbital compute nodes. Published on By Elon Musk confirmed that Starmind will be the official name of SpaceX’s planned AI satellite constellation, following a trademark filing by xAI that surfaced earlier this week. Starmind is what’s being described to the FCC as a constellation of up to one million AI satellites It’s worth noting that SpaceX’s Starlink communication satellite and Starmind are built on the same orbital infrastructure concept but serve entirely different purposes. Starlink is a connectivity network, with satellites receiving and relaying data between points on Earth, and functioning as a high-speed internet backbone in space. The satellites themselves do not process or think, and move information from one place to another, the same function a fiber cable performs underground. SpaceX just forced Verizon, AT&T and T-Mobile to team up for the first time in history Starmind, on the other hand, is something completely different, and tather than moving data, its satellites would compute data through artificial intelligence and directly in orbit using onboard processors powered by large solar arrays. Where a Starlink satellite is essentially a very fast pipe, a Starmind satellite is a server. The practical implication is that Starmind would allow AI models to run inference, process queries, and generate outputs from space, then beam results down to users anywhere on Earth within milliseconds, and without the data ever needing to travel to a terrestrial data center.Advertisement Starship will be able to carry 30 to 50 AI1 satellites per launch, delivering the equivalent of dozens of server racks per flight, with no land acquisition, no power grid approval, and no cooling infrastructure required on the ground. SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat. The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement It’s worth noting that SpaceX’s Starlink communication satellite and Starmind are built on the same orbital infrastructure concept but serve entirely different purposes. Starlink is a connectivity network, with satellites receiving and relaying data between points on Earth, and functioning as a high-speed internet backbone in space. The satellites themselves do not process or think, and move information from one place to another, the same function a fiber cable performs underground. SpaceX just forced Verizon, AT&T and T-Mobile to team up for the first time in history Starmind, on the other hand, is something completely different, and tather than moving data, its satellites would compute data through artificial intelligence and directly in orbit using onboard processors powered by large solar arrays. Where a Starlink satellite is essentially a very fast pipe, a Starmind satellite is a server. The practical implication is that Starmind would allow AI models to run inference, process queries, and generate outputs from space, then beam results down to users anywhere on Earth within milliseconds, and without the data ever needing to travel to a terrestrial data center.Advertisement Starship will be able to carry 30 to 50 AI1 satellites per launch, delivering the equivalent of dozens of server racks per flight, with no land acquisition, no power grid approval, and no cooling infrastructure required on the ground. SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat. The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement SpaceX just forced Verizon, AT&T and T-Mobile to team up for the first time in history Starmind, on the other hand, is something completely different, and tather than moving data, its satellites would compute data through artificial intelligence and directly in orbit using onboard processors powered by large solar arrays. Where a Starlink satellite is essentially a very fast pipe, a Starmind satellite is a server. The practical implication is that Starmind would allow AI models to run inference, process queries, and generate outputs from space, then beam results down to users anywhere on Earth within milliseconds, and without the data ever needing to travel to a terrestrial data center.Advertisement Starship will be able to carry 30 to 50 AI1 satellites per launch, delivering the equivalent of dozens of server racks per flight, with no land acquisition, no power grid approval, and no cooling infrastructure required on the ground. SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat. The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement Starmind, on the other hand, is something completely different, and tather than moving data, its satellites would compute data through artificial intelligence and directly in orbit using onboard processors powered by large solar arrays. Where a Starlink satellite is essentially a very fast pipe, a Starmind satellite is a server. The practical implication is that Starmind would allow AI models to run inference, process queries, and generate outputs from space, then beam results down to users anywhere on Earth within milliseconds, and without the data ever needing to travel to a terrestrial data center.Advertisement Starship will be able to carry 30 to 50 AI1 satellites per launch, delivering the equivalent of dozens of server racks per flight, with no land acquisition, no power grid approval, and no cooling infrastructure required on the ground. SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat. The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement Starship will be able to carry 30 to 50 AI1 satellites per launch, delivering the equivalent of dozens of server racks per flight, with no land acquisition, no power grid approval, and no cooling infrastructure required on the ground. SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat. The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat. The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet. Advertisement How to give your Tesla a Custom Lovk Sound! Easy tutorial!! #tesla #teslatok #teslalocksound Copyright © TESLARATI. All rights reserved.

This material may not be published, broadcast, rewritten, or redistributed. ©2026 FOX News Network, LLC. All rights reserved. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset. Powered and implemented by FactSet Digital Solutions. Legal Statement. Mutual Fund and ETF data provided by LSEG. Tech expert Kurt Knutsson says Hugging Face has launched affordable open-source humanoid robots for home AI exploration. NVIDIA, a technology company known for AI computing and robotics systems, has introduced NVIDIA Halos for Robotics. The company calls it the industry's first full-stack, comprehensive safety system for robotics and physical AI. Physical AI refers to machines that can sense, decide and act in the real world. NVIDIA says Halos brings together AI compute, safety software, sensor data, safety applications and inspection. The goal is to give robots a common safety architecture before they work near people. Sign up for my FREE CyberGuy Report HUMANOID ROBOTS ARE GETTING SMALLER, SAFER AND CLOSER NVIDIA introduced Halos for Robotics, a full-stack safety system designed to help robots operate more safely alongside people in industrial workplaces. (Photo by Tomohiro Ohsumi/Getty Images) NVIDIA Halos for Robotics is a safety system for robots and physical AI. NVIDIA says it connects the key layers needed to build, validate and deploy robotic systems. That includes AI compute, system software, sensor data, safety applications and inspection. In other words, NVIDIA wants robot makers to build safety into the stack from the beginning. That approach becomes important as robots start operating in places where people are already working. NVIDIA says the system draws on more than 18,600 engineering years of autonomous vehicle safety development. That background is important because robots and autonomous vehicles face a related challenge. Both need to sense what is happening around them, make decisions quickly and operate more safely around people. The next generation of autonomous robots will operate in dynamic environments. A warehouse floor may have workers walking nearby, equipment moving through shared spaces and other robots doing their own jobs. That creates a tough safety challenge. A robot needs sensors to understand what is happening around it. Then it needs AI compute to process that information. After that, it needs safety software that can help control behavior in real time. NVIDIA says Halos gives robotics companies a standardized safety architecture that connects the main safety layers instead of leaving each part to operate separately. "Physical AI is transforming how factories, warehouses and logistics operations work, and robotics teams need a unified safety architecture to scale autonomous systems into these environments," said Deepu Talla, vice president of robotics and edge AI at NVIDIA. "With NVIDIA Halos for Robotics, developers and system builders can harness NVIDIA's proven autonomous vehicle safety foundation to develop safer robots faster and bring them into industrial operations alongside workers with greater confidence." NVIDIA Halos for Robotics spans several layers of robot safety. NVIDIA IGX Thor and NVIDIA Holoscan Sensor Bridge provide industrial-grade AI compute, built-in safety and sensor connectivity for real-time robotics and safety workloads. NVIDIA Halos OS provides the software stack for robotics safety. That includes Halos Core, which supports safety-related operating functions. The system also includes safety applications built with the NVIDIA Halos Outside-In Safety Blueprint. That blueprint uses external cameras and AI agents to help robots adjust behavior in industrial settings. Then there is the NVIDIA Halos AI Systems Inspection Lab. NVIDIA describes it as the world's first ANSI National Accreditation Board-accredited program for physical AI functional and AI safety. The lab helps partners prepare Halos integrations for third-party certification from groups including TÜV Rheinland, UL Solutions, TÜV SÜD, exida, SGS and CertX. Agility is the first company to use NVIDIA Halos for Robotics. Its humanoid robot Digit is designed for industrial work in logistics, manufacturing and warehouse settings. Customers include Amazon, GXO, Schaeffler and Toyota Motor Manufacturing Canada. Agility is working with NVIDIA to integrate NVIDIA IGX Thor and Halos Core into Digit's proprietary safe human detection system. NVIDIA IGX Thor provides industrial-grade AI compute with built-in safety capabilities, while Halos Core supports safety-related operating functions. The robotics company will also participate in the NVIDIA Halos AI Systems Inspection Lab. Together, Agility and NVIDIA plan to use the lab to help ensure Digit's safety software, AI components and cybersecurity protections meet standards such as IEC 61508, ISO 13849 and ISO/IEC TR 5469 before final third-party certification. "Safety has to be built into the robot and validated across the entire system," said Peggy Johnson, CEO of Agility. "Partnering with NVIDIA to implement and optimize the Halos for Robotics system extends our leadership in responsible automation, which is a non-negotiable requirement for bringing humanoids safely into industrial workflows." HUMANOID ROBOT SWAPS ITS OWN BATTERY TO WORK 24/7 NVIDIA says its new Halos platform combines AI compute, safety software, sensor data and certification tools to create a common robotics safety architecture. (Photo by Ding Hongfa/Xinhua via Getty Images) NVIDIA says the Halos for Robotics ecosystem includes partners across software, embedded systems, sensors, silicon, industrial applications and certification bodies. Software partners include Acontis, Amazon FreeRTOS and QNX. Those software partners support real-time operating environments, safety communications and embedded software for functional safety development. Embedded systems partners include Advantech and NexCobot, which deliver safety-designed IGX-based systems for robotics deployments. Sensor and silicon partners include Infineon, NXP, SICK, STMicroelectronics and Texas Instruments. Industrial application partners include FORT Robotics, Inventec, KION Group and Neurealm, which are developing functional safety agents using the NVIDIA Halos Outside-In Safety Blueprint. Certification bodies also play a role. TÜV Rheinland is inspecting NVIDIA IGX Thor, Halos OS and Holoscan Sensor Bridge for functional safety certification readiness. NVIDIA says this builds on TÜV SÜD's inspection of Thor SoC and certification of Halos Core for ISO 26262. The NVIDIA Halos AI Systems Inspection Lab includes more than 40 companies across manufacturers, certification bodies and safety vendors. NVIDIA Halos Core for NVIDIA IGX is available in early access for registered developers. It supports Linux and Linux plus QNX configurations. The open source NVIDIA Halos Outside-In Safety Blueprint is also available in early access on GitHub. It is part of the Halos Applications layer of Halos OS. Based on NVIDIA's announcement, this is built for developers, robotics companies and industrial operations trying to bring physical AI into real workplaces. If humanoid robots are going to work around us, safety needs to be more than a promise in a polished demo. NVIDIA is trying to make robot safety more standardized before humanoids show up in more industrial spaces. That could help companies move faster. However, it also raises a bigger question: who decides when a robot is safe enough to work beside humans? The certification part is what really stands out to me. NVIDIA is talking about safety software, AI components and cybersecurity protections before final third-party certification. That is the kind of detail people should be asking about now, not after robots are already on the warehouse floor. But let's be real here. A humanoid robot that looks impressive on stage has a very different job once it enters a real workplace. Workers will want to know how it sees them, how it reacts to sudden movement and what it does when something goes wrong. ROBOT SOCCER PLAYER DENTS WALL WITH TERRIFYING KICKS Agility Robotics is the first company integrating NVIDIA Halos into its Digit humanoid robot for warehouse and manufacturing environments. (Photo by VCG/VCG via Getty Images) NVIDIA is making a smart move here. If humanoid robots are going to work near people, safety has to be built into the whole system from the start. Agility's use of Halos for Robotics shows where this is headed. What stands out to me is the certification readiness. NVIDIA is talking about software, AI components and cybersecurity protections before final third-party certification. That is the kind of detail people should ask about before any company says a robot is ready to work beside humans. Would you feel comfortable working next to a humanoid robot if a company said its safety system had been inspected and prepared for certification? Let us know by writing to us at Cyberguy.com Sign up for my FREE CyberGuy Report CLICK HERE TO DOWNLOAD THE FOX NEWS APP Copyright 2026 CyberGuy.com. All rights reserved. Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com. Stay up-to-date on the latest AI technology advancements and learn about the challenges and opportunities AI By entering your email and clicking the Subscribe button, you agree to the Fox News Privacy Policy and Terms of Use , and agree to receive content and promotional communications from Fox News. You understand that you can opt-out at any time. Subscribed You've successfully subscribed to this newsletter! This material may not be published, broadcast, rewritten, or redistributed. ©2026 FOX News Network, LLC. All rights reserved. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset. Powered and implemented by FactSet Digital Solutions. Legal Statement. Mutual Fund and ETF data provided by LSEG.

Robots may be the new best friend for forgetful humans. MIT researchers have developed a long-term memory framework for robots that can help them build a detailed mental model of large, complicated spaces. The system is called DAAAM, short for Describe Anything, Anywhere, Anytime, at Any Moment, and the goal is to let robots remember objects, locations, and details over time. This might not sound headline-grabbing, though robots are still surprisingly bad at something humans do casually. You may remember that your keys were on the kitchen counter last night, or that a half-finished part was left in a factory bin. However, a robot working beside you would struggle to connect that object and location in a useful way. DAAAM tries to fix this by combining two things robots already use, namely computer vision and 3D mapping. As a robot moves through an environment, it attaches detailed language descriptions to objects it sees and stores them in a spatial map. So rather than just knowing there is an object at a coordinate, the robot may remember that there is a red bicycle with a flat tire near a specific building, or that a certain tool was seen in a particular work area. A person could then ask something like, “Where did I leave my wallet?” or “Go grab the component we started assembling last night,” and the robot could search its memory for the right object and location. MIT’s DAAAM can run fast enough for a mobile robot to use in real time. The researchers also found that it answered questions more accurately than current methods, depending on the type of query. Still, this is not a feature coming to your robot vacuum next week. The work was presented at the Conference on Computer Vision and Pattern Recognition, and the paper is available as a preprint. Researchers are still working on improvements, including giving the system better confidence levels and helping it remember significant events in an environment. For now, the idea is pretty interesting. AI is the buzzword now, but intelligence that’s useful in a more real-world way does sound more appealing. A new study from the University of Colorado Boulder confirms something that sounds both impressive and concerning. People find interacting with AI simulations of their dead loved ones deeply meaningful, and most will come away wanting to do it again. The researchers call it a "generative ghost," which is a clear reference to generative AI, but I’d still prefer to call it unsettling. A humanoid robot designed to live in your house, learn your habits, and pick up on your mood without being prompted is no longer science fiction. Shenzhen-based UBTech Robotics unveiled its Uworld U1 series this week, introducing three robots built for companionship rather than factory work or household chores. A body that moves like yours, and a brain that reads how you feel Flipper Devices has built a reputation among hackers and hardware enthusiasts with the Flipper Zero, a pocket-sized gadget capable of interacting with RFID, NFC, Bluetooth, and other wireless protocols. Now, the London-based company is taking a very different approach. Its latest product, the Busy Bar, is a desktop productivity display designed to help users stay focused, signal their availability, and automate parts of their workflow. After being teased last year, the device is finally going on sale on July 14. While the concept is genuinely clever, its starting price of up to $249 may make many buyers think twice. Upgrade your lifestyleDigital Trends helps readers keep tabs on the fast-paced world of tech with all the latest news, fun product reviews, insightful editorials, and one-of-a-kind sneak peeks.

Oops, something went wrong Computer vision is part of our automated future — and present. Aeva Technologies (NASDAQ:AEVA) and Cognex Corporation (NASDAQ:CGNX) offer different paths into the computer vision market. One is a speculative high-growth play, while the other provides steady returns from established industrial technologies. Aeva is pioneering next-generation sensing technology, while Cognex dominates the mature field of machine vision. Investors often compare them because both are essential for the expansion of robotics and automation. While their technologies overlap in purpose, their financial stages and risk profiles are worlds apart for your portfolio. Aeva sells 4D lidar-on-chip systems that combine sensing and processing on a single silicon chip. Its primary markets include automated driving, robotics, and consumer devices. The company is part of the fast-growing tech stocks landscape, targeting automated driving and robotics. Customer concentration like this adds a layer of risk to the business, as the top three customers accounted for 64% of total revenue in 2025, although that reliance is decreasing. In 2024, Aeva's top two customers accounted for 74% of revenue. In FY 2025, revenue reached $18.1 million, up from approximately $9.1 million in the previous year. Despite this 99.4% revenue growth, the company reported a net loss of approximately $145.4 million. This indicates that while the business is expanding its top line, it remains far from profitability. This high valuation is reflected in a steep P/S ratio, which compares the stock price to its revenue per share to help value companies that do not yet have consistent profits. As of its December 2025 balance sheet, the debt-to-equity ratio stands at roughly 7.7x. This metric, which compares total debt to shareholder equity, suggests a high level of leverage relative to its net worth. The current ratio is approximately 4.3x, showing the company has enough short-term assets to cover its immediate liabilities. Free cash flow was negative at nearly $119.7 million, as the company spent more on operations and equipment than it brought in from sales. Cognex provides machine vision products that allow robots and automated systems to interpret their surroundings. The company serves the automotive, logistics, and electronics industries, which require high-precision automation. Large customers occasionally account for a material portion of revenue, which can affect pricing power and net margins. The loss or significant reduction of orders from these major clients could significantly hurt the company's financial performance. In FY 2025, revenue reached approximately $994.4 million, up nearly 8.7% from the prior year. The company is consistently profitable, reporting net income of close to $114.4 million for the period. This resulted in a net margin of 11.5%, which measures the percentage of revenue remaining after all expenses are paid. Investors often look at the Forward P/E, a metric that uses future earnings estimates to determine how expensive a stock is relative to its profit potential. As of its December 2025 balance sheet, the debt-to-equity ratio is approximately 0.1x. This indicates a very low level of debt relative to shareholders' equity. The current ratio is close to 3.8x, showing a healthy ability to meet short-term financial obligations. Free cash flow was strong at nearly $236.8 million, providing capital for reinvestment or shareholder returns. This positive cash generation allows the company to fund its own research and development without relying on external debt. Aeva faces significant risks due to its extreme customer concentration, making it vulnerable if a major partner withdraws. The commercial success of its 4D lidar technology remains unproven, and the company may struggle to scale manufacturing through third parties. Competition is also fierce, as automotive giants like Tesla Inc. (NASDAQ:TSLA) or established rivals might develop their own internal sensing solutions. Cognex deals with intense competition in a fragmented market, where low-cost tools could erode its market share. The company is also exposed to geopolitical tensions, particularly regarding its manufacturing operations in China and Vietnam. International trade risks are high, as nearly 67% of its revenue comes from outside the United States, leaving it open to shifts in global trade policy or competition from firms like Amazon.com Inc. (NASDAQ:AMZN) in logistics. Cognex appears more established with a higher Forward P/E, while Aeva carries a significantly higher P/S ratio reflecting its early-stage growth profile. Metric Aeva Technologies Cognex Sector Benchmark Forward P/E 24.9x 41.0x 29.5x P/S ratio 80.2x 10.2x n/a Sector benchmark uses the SPDR XLY sector ETF.Valuation metrics sourced from Financial Modeling Prep (FMP) and may differ from other data providers. Machine vision, in which a computer system uses sensors to "see" objects such as speed limit signs on the highway and pedestrians crossing the street, isn't a terribly new technology. But AI-powered computer vision is the evolution of the technology, enabling machine vision sensors to learn as they go. Aeva Technologies' revenue is still very small at just over $18 million last year, but Wall Street sees the company as quick-growing, with analysts consensus seeing Aeva sales grow to $32.9 million this year and to more than $70 million next year. The fact that very large automakers, including Daimler Truck Holding and Mercedes-Benz Group, are customers is a positive sign, but Aeva's record of posting huge net losses is a concern. Given the competitive nature of the automotive market, Aeva is still too speculative just yet. Cognex Corp. faces the same competitive pressures in the automotive industry as Aeva, but Cognex has the heft and track record that make it the better option in 2026. For one, supplying machine vision to automakers is just a portion of its business, accounting for 19% of 2025 revenue. Three other sectors account for more: logistics (26%), packaging (21%), and consumer electronics (19%), according to the company. Macroeconomic challenges mean the 2026 outlook for Cognex isn't ideal—interest rate hikes, rising energy prices, and possible economic weakness among consumers pose potential risks—but its first quarter 2026 results were better than expected, with revenue and net income both beating expectations. With four times more cash on hand than debt, Cognex is well-positioned to weather any hiccups in demand for the foreseeable future. Before you buy stock in Aeva Technologies, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Aeva Technologies wasn't one of them. The 10 stocks that made the cut are built for long-term growth and could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $439,038!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $1,277,804!* That performance is why people listen. With a track record of beating the S&P 500 by nearly 5x, Stock Advisor offers a distinct advantage. Don't miss the latest top 10 list, available with Stock Advisor, and join an investing community built for the long haul. See the 10 stocks » *Stock Advisor returns as of June 10, 2026. Brendan Coffey has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Amazon, Cognex, and Tesla. The Motley Fool has a disclosure policy. Aeva Technologies vs. Cognex: Which Computer Vision Stock Is a Better Buy in 2026? was originally published by The Motley Fool

Computer vision vendor lists rank companies by size, ratings, or brand recognition, but this doesn’t tell you much about whether a vendor will stay involved after the model is trained or whether they can handle the environment where your product runs. The gap between “we built the model” and “it works in production” is where many CV projects break down. After all, a model that performs well in a controlled test setting may behave very differently on a factory floor, on a camera chip, or in a live video stream with changing lighting. Closing this gap takes either strong internal engineering on your side or a vendor whose work goes beyond training the model. Some computer vision vendors train a model and hand it off. Others also build the deployment infrastructure. A smaller group can handle the full stack, including hardware, firmware, the model, cloud systems, and post-launch monitoring. The right choice depends less on who is biggest or best known and more on which scope fits your project stage and your team’s capacity. To help you choose the right computer vision development company depending on your requirements, several teams below were selected for clear delivery track records, distinct service models, and industry experience across real buyer scenarios, including embedded IoT, autonomous vehicle vision, industrial defect detection, OCR automation, and cloud-native enterprise systems. Computer vision companies describe their services in similar terms, but what they deliver can vary. In most cases, their work falls into one of three scopes. If you choose a model-only vendor when your project needs infrastructure or full-cycle delivery, you’ll likely end up with months of extra work after handoff. That’s why service scope should be your first filter. The table below compares all companies by founding year, team size, CV service scope, and core industry focus. Service scope varies a lot across the list, from full-cycle hardware-to-cloud engineering to model-focused work where the client handles infrastructure. Some companies have been delivering since 2000, while others launched their AI practice much later. Team sizes range from 100 to more than 4,000 engineers. If you want a quick way to rule vendors in or out before reading the full profiles, this is the fastest place to start. Company Founded Team size CV service scope Industry focus SQUAD 2016 700+ Full-cycle: hardware + model + cloud Security cameras, ADAS, IoT Tooploox 2012 ~200 Model + research + integration AV, medical, industrial Lemberg Solutions 2007 160 Model + embedded + IoT integration Healthcare, agritech, sportstech Simform 2010 1,000+ Model + cloud infrastructure SaaS, enterprise, cloud-native instinctools 2000 250+ Model + consulting + integration Manufacturing, robotics, retail Azumo 2016 100+ Model + infrastructure + MLOps Media, manufacturing, healthcare BairesDev 2009 4,000+ Model + staff augmentation Enterprise across industries Chudovo 2006 100+ Model + integration Logistics, security, finance SQUAD is one of the best computer vision development companies for AI-powered smart camera products. The team works across the full stack, including hardware design, firmware, edge AI, ISP tuning, cloud streaming, and mobile integration. With more than 700 engineers and a 6,500 m² Innovation Lab, SQUAD has delivered 500+ projects, 50+ devices, 100+ app releases, and 20+ AI features to production. One example is a long-running edge CV program for a global consumer electronics client, where SQUAD built and optimized detection models across Ambarella, Qualcomm, OmniVision, and SigmaStar platforms. The team replaced a third-party motion-detection library with an on-device deep learning model, reduced recurring cloud licensing costs, and helped raise the product’s customer rating to above 4 stars. Fits best when: your product is a physical camera or edge device, and you need one team to own the work from PCB design through post-launch model monitoring. Tooploox, part of Solvd Inc. and founded in 2012, is an AI-focused software company with around 200 professionals and an R&D group of more than 40 people. Its team has produced 30+ publications at conferences such as NeurIPS, ICML, and ECCV. Research partners include Stanford, Carnegie Mellon, ETH Zurich, and Imperial College London. Tooploox has worked on stereoscopic vision cameras for autonomous vehicles and helped build an AI-ready digital histopathology platform for cancer research with a leading European university. Fits best when: your project sits between research and production, and you need a team that can design from first principles rather than rely on off-the-shelf models. Lemberg Solutions is an engineering and consulting company based in Lviv, Ukraine, with 160 engineers, more than 15 years of delivery experience, and 300+ completed projects for clients in Europe and North America. The company has spent seven years working specifically in computer vision. Clutch reviewers often mention strong ownership, responsiveness, and active involvement in the design process. The prominent example of their work is Cavamo, where Lemberg helped turn a computer vision concept into a baseline MVP with structured milestone tracking and design input. Its work with Bosch Rexroth also strengthens its enterprise engineering profile. Fits best when: your project combines embedded software and computer vision, and you want a mid-size partner that can own the product rather than stop at the model layer. Simform is a digital engineering company ranked #3 globally in Clutch’s Spring 2025 rankings out of more than 41,000 firms. It holds a 4.8 rating from 84 reviews and focuses on cloud, data, AI/ML, and product engineering. Its clients include Google, Red Bull, and Sony. Clutch reviews frequently mention Simform’s technical strength and on-time delivery. The company operates under a co-engineering model, meaning its team integrates closely with the client’s delivery process. Fits best when: your computer vision feature is part of a SaaS or enterprise software product, and you need a vendor that can handle both the model and the surrounding cloud infrastructure. instinctools is a software development and consulting company founded in 2000, with offices in Maryland, USA, and Stuttgart, Germany. It holds Microsoft Certified Partner status and French CIR certification, which can support R&D tax efficiency for eligible projects. Its case studies cover defect detection, manufacturing analytics, and quality inspection workflows. The company also works on robot-related computer vision projects that require both model development and integration with hardware and communications systems. Fits best when: your project is in industrial automation or manufacturing, and you want a partner with long delivery experience and strong engineering discipline. Azumo is an AI development company that has delivered more than 100 AI projects under SOC 2 compliance since 2016. Its clients include Meta, Discovery Channel, Zynga, Omnicom, and Twitter. Every project begins with a paid two- to three-week discovery phase to assess data readiness, infrastructure, and feasibility before full development starts. Azumo has built enterprise AI systems for major clients, including a custom search solution for Meta. Reviewers also describe its delivery as thorough and reliable. Fits best when: you’re a US-based company looking for nearshore delivery, SOC 2-aligned processes, and a structured discovery phase before committing to full build costs. BairesDev is a Latin American nearshore engineering company with 62 Clutch reviews, project sizes ranging from $10,000 to more than $1 million, and client references that include Google, IBM, and Rolls-Royce. Reviewers consistently highlight strong project management, high-quality senior engineering, and smooth integration with internal teams. BairesDev is chosen by companies that need to quickly expand their AI teams without slowing down delivery. Fits best when: you need to scale a computer vision team quickly with senior nearshore talent and want flexibility across staff augmentation and broader engineering support. Chudovo is a custom software development company with 22 Clutch reviews, more than 190 completed projects, and hourly rates ranging from $25 to $49. It serves industries including healthcare, logistics, finance, security, and defense. Its computer vision team works with annotation tools, GPU servers, edge controllers, and embedded devices. One example is a warehouse video analytics system that identified brands using visual markers and automatically counted incoming goods, helping reduce manual tracking and improve receipt accuracy. Fits best when: your scope is clear, such as OCR, surveillance analytics, or inventory tracking, and you need a reliable mid-market partner with experience in logistics and security use cases. The market for computer vision development companies goes far beyond the usual shortlist. The 8 vendors above cover a wide range of service scopes, from model-only delivery to full hardware-to-cloud execution. SQUAD is the strongest fit when edge hardware integration is the main challenge. Tooploox is an option for research-heavy computer vision work without clear benchmarks. Simform, Azumo, and BairesDev are better suited to cloud-native enterprise projects, each with its own delivery model and pricing. Chudovo and Lemberg Solutions are solid choices for more defined mid-market builds at Eastern European rates. Go to top Disclaimer: This article contains sponsored marketing content. 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Please ensure that JavaScript is enabled in your browser to view this page. When a robot, drone, or other autonomous system needs a clear understanding of its surroundings, developers often choose to equip it with a depth camera. These sensors make it possible to measure the distance to nearby objects and build a three-dimensional map of the environment, helping machines navigate, avoid obstacles, and interact with the world around them. The downside is that adding a depth camera is only the beginning — engineers will still need to develop the algorithms and control systems that are powered by the data these cameras produce. The latest release from RealSense — the D585 — was built to simplify computer vision applications. Not only does it provide better image quality than past models, but it also does a lot of image processing on-device. That frees developers up to create without worrying about every single little detail. At the core of the D585 is a new RealSense V5 system-on-chip that combines a depth engine, image signal processor, DSP, AI acceleration hardware, and a quad-core Arm processor into a single package. Instead of streaming raw data to a host computer and expecting it to handle everything, the camera can perform many tasks internally. RealSense describes the device as an AI-native, software-defined vision platform that will continue to gain new capabilities through SDK updates after deployment. The stereo depth camera features dual infrared projectors, high-resolution sensors, global shutter imaging, and integrated IR filters that allow it to operate both indoors and outdoors. A 120° by 100° field of view gives robots a broad view of their surroundings, while depth output resolutions reach up to 1280 × 960 pixels. Full-resolution operation is supported at up to 60 frames per second, with depth frame rates reaching as high as 90 frames per second. RealSense says the D585 can measure depth at distances as short as 10 cm at full resolution, making it suitable for applications like robotic manipulation, inspection systems, and collaborative robot arms working near objects. On the opposite end of the spectrum, the company lists an ideal operating range beyond 10 meters and a maximum range exceeding 20 meters, allowing the same camera to support mobile robots in large warehouses and industrial facilities. The D585 also introduces features designed to reduce system complexity. A Dual RGB mode can output synchronized RGB and depth streams simultaneously while handling stream alignment directly on the camera. RealSense has also developed a Depth Compression feature that reduces bandwidth requirements by approximately 75 percent, which could be useful when streaming high-frame-rate depth data across bandwidth-constrained links. Additional capabilities are planned through the RealSense SDK ecosystem. Person detection will be available in beta at launch, while future updates are expected to add features such as visual-inertial odometry, occupancy grid generation, auto-calibration, face detection, and enhanced depth processing. RealSense expects the camera to begin shipping in the first quarter of 2027. Pricing has not yet been announced. Hackster.io, an Avnet Community © 2026

Oops, something went wrong NVIDIA Corporation (NASDAQ:NVDA) is one of the 14 AI Stocks Making Moves on Wall Street: Nvidia, Micron, and More. On June 29, Bernstein reiterates Nvidia as "Outperform," stating that the AI chipmaker is a key player in the humanoid space. The firm highlighted both Nvidia and Qualcomm are at the forefront of developing advanced processors that serve as the central processing units for robotics. Compute and software platform providers also have a clear path to building moats with NVDA and QCOM at the forefront of developing processors that function as the 'brain' of the robot, allowing it to process sensor data, reason, plan, and execute actions quickly. NVIDIA Corporation (NASDAQ:NVDA) specializes in AI-driven solutions, offering platforms for data centers, self-driving cars, robotics, and cloud services. While we acknowledge the potential of NVDA as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: 33 Stocks That Should Double in 3 Years and Cathie Wood 2026 Portfolio: 10 Best Stocks to Buy. Disclosure: None. Follow Insider Monkey on Google News.

Oops, something went wrong Given its significant hedge fund backing and attractive upside potential, NVIDIA Corporation (NASDAQ:NVDA) is one of the most promising robotics stocks. NVIDIA Corporation (NASDAQ:NVDA) is extending its physical AI ambitions on two fronts simultaneously, pushing deeper into industrial robotics safety while advancing its push into agentic life sciences.On June 23, 2026, NVIDIA Corporation (NASDAQ:NVDA) announced the NVIDIA BioNeMo Agent Toolkit, a platform that equips AI agents with domain-specific life sciences tools spanning protein structure prediction, molecular docking, generative chemistry, genomic analysis, and biomarker discovery.The toolkit draws on more than a decade of NVIDIA life sciences libraries and is powered by NIM microservices, Parabricks, NeMo, and Nemotron technologies. More than 50 companies are already using it, including Anthropic, OpenAI, Lilly, and Natera, with collaborations also spanning scientific data platforms, lab automation companies, and AI-native biology firms.NVIDIA Corporation (NASDAQ:NVDA) said the toolkit can compress virtual screening timelines from days to minutes and has already delivered 2x faster performance for protein design models like RosettaFold3 through a collaboration with the University of Washington's Institute for Protein Design.A day earlier, NVIDIA Corporation (NASDAQ:NVDA) announced NVIDIA Halos for Robotics, described as the industry's first full-stack safety system for robotics and physical AI, drawing on more than 18,600 years of engineering experience in autonomous vehicle safety development. The system spans AI compute, safety software, sensor data, and an ANAB-accredited inspection lab for functional and AI safety certification.Agility, whose humanoid robot Digit operates in facilities for Amazon, GXO, Schaeffler, and Motor Manufacturing Canada, is the first company to integrate NVIDIA Halos for Robotics into its safety architecture.NVIDIA Corporation (NASDAQ:NVDA) is a fabless semiconductor and AI computing company that designs GPUs, AI accelerators, Application Programming Interfaces (APIs), and system-on-a-chip units. Through its CUDA ecosystem, the company enables industries ranging from autonomous vehicles to scientific research by advancing AI, accelerated computing, and data center infrastructure.While we acknowledge the potential of NVDA as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock.READ NEXT: 33 Stocks That Should Double in 3 Years and 15 Stocks That Will Make You Rich in 10 Years.Disclosure: None. Follow Insider Monkey on Google News.

Authored by Jijo Malayil via Interesting Engineering, Chinese firm Alibaba has launched its first embodied AI model family, which links large language models with real-world robotic actions. The Qwen-Robot suite was developed by Alibaba's Tongyi Lab and is undergoing pilot testing with selected Alibaba Cloud enterprise clients. The suite comprises three models focused on navigation, manipulation, and world modeling for robots operating in physical environments. Alibaba said the models enable machines to perceive, reason, and interact with the real world, joining a growing global push to advance embodied AI beyond traditional chatbot applications. Alibaba says its Qwen family of AI models has become very good at understanding the physical world. These models can recognize objects, understand spatial relationships, follow complex visual instructions, and reason about real-world environments. For example, a model can understand a command such as, "Go to the kitchen, find the red cup, pick it up, and place it on the shelf." However, understanding a task is different from actually performing it. While a vision-language model (VLM) can describe the steps needed to complete a task, it cannot directly control a robot's movements. The challenge is connecting human language and visual understanding with the motor actions required to interact with the physical world. This problem is difficult because robot training data is very different from internet data. Information collected from navigation systems, robotic arms, vehicles, and cameras comes in different formats and is expensive to gather. Simply combining all this data often creates conflicts rather than improving performance. To address this, Alibaba developed the Qwen-Robot Suite, which includes three specialized models. Qwen-RobotNav focuses on movement and navigation. It helps robots follow instructions, navigate to locations, track targets, and support autonomous driving. According to its website, Qwen-RobotManip focuses on physical interaction. It enables robots to grasp, move, and manipulate objects using a large training dataset collected from different robotic systems. Qwen-RobotWorld acts as a world model, predicting how environments may change and helping robots understand the likely outcomes of their actions. Together, these models aim to enable robots to understand instructions, interact with objects, navigate environments, and make decisions in the real world. Alibaba showcased Qwen-RobotNav on a Unitree Go2 quadruped powered by NVIDIA Jetson Thor hardware and a single low-resolution camera. The robot successfully navigated an unfamiliar apartment, following spoken instructions across multiple rooms without preloaded maps, while maintaining an inference latency of 196 milliseconds. The company claims that Qwen-RobotManip, its robotic manipulation model, was trained on more than 38,000 hours of open-source data covering object handling and interaction tasks. According to Alibaba, the model recently achieved the highest score in the generalist category of the RoboChallenge real-world robotics benchmark, earning a process score of 59.83 and a task success rate of 45 percent. The company also unveiled Qwen-RobotClaw, a robotics agent framework that enables Qwen models to use the Qwen-Robot suite as physical-world tools. In one demonstration, an agent searched for a restroom, identified an out-of-order sign, and independently rerouted to another location. Alibaba further open-sourced Chat2Robot, a browser-based platform for testing embodied AI interactions. As competition in embodied AI intensifies worldwide, Alibaba has expanded its ambitions beyond language and multimodal software with the launch of its Qwen-Robot models. The move reflects a broader industry shift toward creating AI systems capable of understanding and interacting with the physical world. Alibaba's move comes as competition in physical AI accelerates globally. In the US, Google DeepMind is advancing Gemini Robotics, while Nvidia is expanding its robotics ecosystem through Cosmos, Isaac, and GR00T. Start-ups, including Physical Intelligence, Skild AI, and Figure AI, are also developing general-purpose robotic intelligence, according to the South China Morning Post. China is strengthening its position by pairing its manufacturing advantages with growing investments in AI software for autonomous decision-making. The sector now spans AI developers, robotics firms, and EV makers. Companies such as Alibaba, Tencent, Unitree, AgiBot, UBTech, Galbot, Spirit AI, GigaAI, Xpeng, and Xiaomi are actively pursuing embodied AI technologies. 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They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their hiding spots. It’s the latest effort by the South Florida Water Management District to eliminate as many pythons as possible from the Everglades, where they are decimating native species with their voracious appetites. In Everglades National Park, officials say the snakes have eliminated 95% of small mammals as well as thousands of birds. “Removing them is fairly simple. It’s detection. We’re having a really hard time finding them,” said Mike Kirkland, lead invasive animal biologist for the water district. “They’re so well camouflaged in the field.” The water district and University of Florida researchers deployed 120 robot rabbits this summer as an experiment. Previously, there was an effort to use live rabbits as snake lures but that became too expensive and time-consuming, Kirkland said. The robots are simple toy rabbits, but retrofitted to emit heat, a smell and to make natural movements to appear like any other regular rabbit. “They look like a real rabbit,” Kirkland said.. They are solar powered and can be switched on and off remotely. They are placed in small pens monitored by a video camera that sends out a signal when a python is nearby. “Then I can deploy one of our many contractors to go out and remove the python,” Kirkland said. The total cost per robot rabbit is about $4,000, financed by the water district, he added. Pythons are not native to Florida, but have become established in the swampy, subtropical Everglades by escaping from homes or by people releasing them when they become overgrown pets. A female python can lay between 50 and 100 eggs at a time with a gestation period of 60-90 days, according to the Florida Fish and Wildlife Conservation Commission. It’s not easy to find definite estimates of the number of pythons in Florida. The U.S. Geological Survey recently reported a ballpark number of “tens of thousands,” while other official estimates run as high as 300,000 snakes. They have few natural enemies, although there are occasional confrontations with alligators, and other predators, such as bobcats and coyotes, will eat their eggs. Since 2000, more than 23,000 of the snakes have been removed from the wild, the wildlife commission says. The robot rabbits are the latest attempt to tackle snakes that average between 10 and 16 feet (3 to 5 meters) in length when fully grown. “Every invasive python that is removed makes a difference for Florida’s environment and its native wildlife,” said Ron Bergeron, a member of the water district governing board. Pythons can be humanely killed year-round on private lands and on lands managed by the wildlife commission across the state. Each year the commission holds a “Florida Python Challenge” that carries cash prizes for most pythons caught, the longest snake and so forth. This year, 934 people from 30 states took part in the effort in July, capturing 294 pythons with a top prize of $10,000 to a participant who bagged 60 of the reptiles. It’s too early to determine how successful the robot rabbit project will be, but officials say initial results are a cause for optimism. “This part of the project is in its infancy,” Kirkland said. “We are confident, though, that this will work once we are given enough time to work out some of these details.” (AP) You must be logged in to post a comment. Popular Posts

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Viaggio d’autore tra pulp, citazioni, pop e controversie su Israele L’altro ispettore: ascolti tv e recensione della nuova serie di Rai 1 Sandokan torna in TV con Can Yaman: recensione della serie-evento 2025 The Neighbourhood tornano con tre nuovi singoli, un EP e un tour internazionale Tumori, vescica: Magenta (Palinuro), ‘bene applicabilità effettiva di novità terapeutiche’ Tumori, endometrio: Bignami (Loto), ‘riclassificazione neoplasia ha aperto a medicina di precisione’ Tumori, endometrio: oncologa Lorusso, ‘la sola neoplasia ginecologica con incidenza e mortalità in Calciomercato, al via la sessione estiva Tumori: oncologo Gridelli, ‘immunoterapia in fase precoce riduce rischio di recidive’ COMUNICATO STAMPA – CONTENUTO PROMOZIONALE CHICAGO, June 22, 2026 /PRNewswire/ — Vention, the leading digital-first industrial automation platform, and FANUC America, the global leader in industrial robotics and factory automation, today announced an expanded collaboration focused on simplifying industrial robot deployment through AI-powered programming, digital twin technology, and modular automation. The collaboration combines FANUC’s industrial and collaborative robot portfolio with Vention’s unified automation platform, enabling manufacturers to design, simulate, deploy, and operate robotic systems from a unified environment. The expanded platform now supports multiple FANUC robot families, including CRX collaborative robots, LR Mate industrial robots, LR-10iA series robots, M-710iD series robots, and M-20iD series robots. By integrating collision-free path planning , no-code and Python programming capabilities, the companies are helping manufacturers reduce commissioning complexity while accelerating automation adoption. Powered by Foundation Stereo, an NVIDIA Isaac open model that helps robots see depth using stereo cameras, the system generates a real-time 3D understanding of the workspace using a zero-shot stereo depth estimation, enabling MachineMotion AI to build a digital twin and automatically compute collision-free robot paths These capabilities now extend across FANUC’s robotic portfolio, enabling manufacturers to deploy a wide range of applications, from machine tending and pick-and-place to palletizing, welding, and high-speed industrial automation. Unlike traditional robotic systems that require extensive manual programming and custom integration, the combined solution enables users to design automation systems using modular, pre-validated components that work together seamlessly. Manufacturers can easily generate and validate logic, realistically simulate robotic motion, and test cell interactions before deployment, significantly reducing integration risk and accelerating production ramp-up. “By extending Vention’s platform to FANUC industrial robots, enterprise manufacturers in heavy industry can move from automation design to production much faster,” said Étienne Lacroix. “Combined with FANUC’s proven reliability and long-standing track record in industrial automation, this partnership gives manufacturers a dependable path to scaling production automation.” “We’re seeing strong demand from manufacturers for automation that’s easier to deploy and faster to bring online, especially as labor challenges continue,” said Dick Motley, Director, Authorized System Integrator Network at FANUC America. “Many companies want to automate but are looking for solutions that reduce complexity and are easier to implement. Vention’s AI-powered platform helps customers deploy FANUC’s industrial and collaborative robots, allowing them to get up and running more quickly while maintaining the performance and reliability they expect from FANUC.” Advancing Goal-Driven Industrial Automation The announcement highlights Vention’s MachineMotion AI and MachineLogic ecosystem, introducing a new approach to robotic programming for both collaborative and industrial robots. Rather than manually programming robot paths waypoint by waypoint, operators simply define start and end targets. The system then automatically scans the workspace and computes the optimal collision-free path between the two points without requiring manual waypoint programming. This goal-driven approach enables manufacturers to deploy more adaptive robotic applications that respond dynamically to changing production conditions, mixed-SKU operations, and evolving factory layouts. The platform enables manufacturers to validate robotic motion and automation logic before deployment while accelerating commissioning and adapting more quickly to changing production requirements. Built on modular hardware and software components, the platform enables faster commissioning and scalable automation deployments that can quickly respond to new SKUs, fixtures, and production layouts. Powered by MachineMotion AI, the system combines machine vision and intelligent motion control to enable flexible robotic automation in dynamic manufacturing environments. Live Demonstrations at Automate 2026 The power of this collaboration is on display this week in Chicago: Contact Christine BoivinChristine.boivin@vention.cc+1.514.293.3423 About Vention Vention is leading the future of industrial automation with the world’s only AI-powered full-stack platform, unifying hardware, software, and physical AI into one seamless experience. With over 25,000 machines deployed worldwide and a community of more than 4,000 factories, Vention enables businesses to design, program, deploy, and operate turnkey or custom automation solutions in just days. Vention brings together intelligent software and modular hardware to deliver automation that works right the first time. Visit Vention to learn more. About FANUC FANUC America Corporation is a subsidiary of FANUC CORPORATION in Japan, and provides industry-leading CNC systems, robotics and ROBOMACHINEs. FANUC’s innovative technologies and proven expertise help manufacturers in the Americas maximize productivity, reliability and profitability. Headquartered in Rochester Hills, Mich., FANUC America has facilities throughout North and South America. Visit www.fanucamerica.com for more information or explore the CRX line of cobots at CRX.FANUCAmerica.com. Connect with FANUC America on YouTube, X, Facebook, LinkedIn and Instagram. 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ByBernard Marr, Contributor. Humanoid robots are rapidly transitioning from science fiction to practical applications, attracting significant investment from tech giants and startups. Their appeal lies in operating in human environments and adapting to diverse tasks, promising solutions for labor shortages and hazardous work. Global momentum is accelerating, with China's ecosystem expanding and major players like 1X Technologies, Agility Robotics, Apptronik, Tesla, and Samsung driving commercial deployment. While still nascent, the focus is on developing safe, reliable, and affordable robots for industrial use, with domestic roles emerging later. This shift, integrating AI, advanced sensors, and manufacturing, marks AI's crucial expansion into the physical world. Humanoid robots are having their ChatGPT moment. For decades, they belonged in science fiction, glossy lab demos and viral videos. Now they are starting to appear in factories, warehouses and test homes, with some of the world’s biggest technology companies racing against ambitious startups to build robots that can work safely alongside people. The appeal is easy to understand. Most robots are designed for one specific task. A humanoid robot, in theory, can operate in spaces built for humans, use tools designed for humans and move between different jobs without the need to rebuild the environment around the machine. And with billions of humanoid robots predicted to walk among us in the coming decades, investors, manufacturers and technology giants are paying attention. If humanoid robots can be produced at scale, they could help tackle labor shortages, take on dangerous work and eventually support everyday tasks in homes, hospitals and care settings. The field is still young, and many bold claims remain unproven. Even so, momentum is building fast. In China, the humanoid robot ecosystem is expanding rapidly. The Ministry of Industry and Information Technology said the country had more than 140 humanoid robot manufacturers by 2025, with over 330 product models, while major players in the U.S., Europe and Asia are pushing prototypes toward commercial deployment. So, who are the companies leading this race, and which names should business leaders be watching? 1X Technologies has attracted attention because of its backing from OpenAI and its focus on humanoid robots for the home. The company has shifted from wheeled industrial robots toward domestic humanoids designed to live and work around people. Its Neo robot is being marketed as a home assistant, which, in demonstrations, has been seen to perform everyday chores such as folding laundry, unloading dishwashers and tidying up clutter. Neo can reportedly be bought for $20,000 or rented for $499 per month, and deliveries are currently expected to begin in 2026. The company’s big bet is that the first mass-market humanoid robots may arrive through the front door rather than the factory gate. That is a bold strategy. Home environments are messy, unpredictable and full of edge cases, which makes them far harder than controlled industrial settings. If 1X can make domestic humanoids reliable and useful, it could help define what a robot assistant in the home will look like. AgiBot is one of the most important Chinese humanoid robotics companies, even though it remains less well-known in the West. Backed by BYD, the company is believed to be among the first to deliver humanoids at serious scale, with reports suggesting it has already delivered 10,000 units. Its Yuanzheng A2 series includes highly dexterous hands capable of delicate tasks such as threading a needle. AgiBot has also generated headlines for endurance and mobility. One of its robots reportedly completed a 106km trek from Suzhou to Shanghai without falling over, setting a world record in the process. The company reflects China’s fast-moving approach to humanoid robotics. Rather than focusing purely on spectacle, AgiBot appears to be moving quickly toward volume, deployment and industrial relevance. Agility Robotics is one of the clearest examples of humanoids moving from lab demos into practical work. Its Digit robot has been developed with warehouse and logistics tasks in mind, and the company has worked with Amazon on exploring how robots could support operations across large fulfillment networks. Digit’s backward-bending legs give it a distinctive look, while also improving stability and lifting ability. Agility has also invested in production capacity. Its RoboFab facility in Oregon is reported to be capable of manufacturing 10,000 humanoid robots a year. Agility is one of the companies trying to prove that humanoids can be manufactured in meaningful numbers and deployed in real operational environments. Apptronik is another serious contender in the industrial humanoid race. The U.S. company’s Apollo robot builds on earlier work carried out for NASA’s Valkyrie robot and has benefited from backing from Google DeepMind. Apollo is designed for factory and logistics work, with swappable hands that can be replaced by tools and hot-swappable batteries that allow it to keep working without long recharge breaks. The robot is already being used on factory floors by companies including Mercedes-Benz. That places Apptronik in a strong position. Rather than presenting humanoid robots as a distant consumer fantasy, it is targeting environments where the business case is more immediate, especially manufacturing, warehousing and repetitive physical work. Boston Dynamics has done more than almost any other company to shape public expectations of what robots can do. Its videos of walking, jumping and highly mobile robots have gone viral for years, sometimes inspiring awe and sometimes giving people the feeling that the future has arrived slightly too early. The company began as an MIT spinout and was later acquired by Hyundai. Its famous Atlas robot has long been a benchmark for humanoid mobility. More recently, Boston Dynamics has focused on moving Atlas toward industrial labor roles and pilot deployments. The company’s challenge is turning world-class robotics engineering into scalable commercial products. Its advantage is credibility. When Boston Dynamics shows a robot doing something difficult, the world pays attention. BYD is best known as one of the world’s largest electric vehicle makers, but it is also becoming an important player in humanoid robotics. Like Tesla, BYD sees humanoid robots as a potential way to increase efficiency in large-scale manufacturing. The company is developing its own robotics capabilities while also partnering with other robotics companies, including AgiBot and UBTech. This makes strategic sense. EV factories are complex, high-volume environments where automation can deliver measurable value. If humanoids can support assembly, logistics and inspection tasks, carmakers have a strong incentive to put them to work. Engine AI is another Chinese manufacturer gaining attention, particularly for the natural movement of its humanoid robots. Many humanoids still move with the stiff, cautious gait that people associate with early robotics. Engine AI has focused on creating robots that move in a smoother and more fluid way. In 2025, it reportedly achieved the first front flip by a humanoid robot. A robot that can move dynamically, recover balance and handle complex physical motion has more potential in real-world environments. Figure AI has quickly become one of the most closely watched startups in humanoid robotics. Its Figure 02 robot has already been used to support vehicle production at BMW’s Spartanburg plant in the U.S., including work connected to the production of tens of thousands of vehicles. The company has also attracted high-profile backing from OpenAI, Nvidia and Microsoft. Fourier began by building medical rehabilitation robotics before expanding into general-purpose humanoid robots. Its GR series, including the GR-1 and newer GR-3, is designed for a wide range of use cases, from warehouse support to clinical research. One of its more distinctive features is a cushioned, soft exterior intended to make the robot feel safer and more approachable around people. That design choice points to an important issue. If humanoid robots are going to work around humans, they need to be trusted. Industrial robots have traditionally been separated from people by cages and safety zones. Humanoids will have to operate much closer to us. Fourier’s healthcare and rehabilitation background gives it useful experience in building machines that interact physically with people. Neura Robotics Germany-based Neura is a frontrunner in Europe’s physical AI race. Its aesthetic appearance is down to the fact that it was designed in collaboration with the studio responsible for the looks of the Porsche 911. The 4NE1 features synthetic “skin” that can sense contact in a way that makes interactions with people safer and more natural. It’s no softy though, being capable of lifting up to 100 kg. Germany-based Neura Robotics is one of Europe’s most interesting companies in what is often called physical AI. Its 4NE1 humanoid has been designed with attention to both function and appearance, including input from the design studio responsible for the Porsche 911. It also features synthetic skin that can sense contact, making interactions with people safer and more natural. Despite its friendly look, 4NE1 is built for serious work and is reportedly capable of lifting up to 100kg. Noble Machines is a California startup founded by former Apple, SpaceX and NASA employees. The company has moved quickly, reportedly putting its first humanoid robots to work at a Fortune 500 company within 18 months. Its focus is on difficult, dirty and dangerous jobs that expose human workers to risk. That is likely to be one of the strongest early use cases for humanoid robots. The first wave of valuable deployments may come in jobs that are physically demanding, repetitive or unsafe. Samsung is approaching humanoid robotics from the perspective of a global manufacturing powerhouse. The company has acquired stakes in a number of robotics firms, including Rainbow Robotics and has announced plans to transform its global production facilities into AI-driven factories by 2030. Humanoid robots are expected to play a central role in that strategy, with possible responsibilities including assembly, logistics and facility management. Samsung has a major advantage that many startups lack: it can manufacture at scale. It also has enormous experience in electronics, sensors, semiconductors and consumer technology. If Samsung decides humanoid robots are a strategic priority, it has the resources and industrial footprint to become a serious force in the market. Sanctuary AI has focused heavily on intelligence and dexterity. Its Phoenix humanoid is designed to excel at fine motor control, which is one of the hardest problems in robotics. Moving around is difficult enough, but using hands with human-like precision is even harder. The company’s stated mission is to create and deploy millions of humanoid robots into industry to help address global labor shortages. Its partnership with Magna International, a major automotive supplier, has already seen its technology deployed in automotive sub-assembly roles. Tesla is one of the most talked-about companies in humanoid robotics, largely because of Elon Musk’s huge ambitions for Optimus. Musk has said that humanoid robots could become more important to Tesla than cars. That sounds extraordinary, but it reflects a clear strategic logic. Tesla has expertise in AI, batteries, electric motors, manufacturing and real-world automation, all of which are relevant to building humanoid robots at scale. Optimus prototypes are regularly demonstrated and are reportedly being piloted internally within Tesla facilities. The big question is whether Tesla can do for humanoid robots what it did for electric vehicles: take an ambitious technology, manufacture it at scale and make it feel inevitable. That remains far from guaranteed, but Tesla’s manufacturing depth makes it impossible to ignore. Toyota has taken a broad and thoughtful approach to robotics. The company is exploring humanoid robots for vehicle assembly while also investing in soft robotics for care and companionship. Through the Toyota Research Institute, it has developed platforms such as Punyo, a soft, squishy humanoid designed to interact safely with people and objects. Toyota’s interest in care robotics is significant as Japan has one of the world’s oldest populations, and many countries face similar demographic pressures. Robots that can safely support older adults, assist caregivers and handle physical tasks in homes could become increasingly valuable. UBTech is another Chinese company that may not be widely known in the West, but it has become an important name in industrial humanoids. The company has focused on factory deployments rather than generating constant media attention. That approach has reportedly helped it build one of the world’s largest datasets of humanoid industrial activity and one of the largest numbers of active humanoid deployments in automotive factories. Data could be a huge advantage in humanoid robotics. Robots improve by learning from real-world tasks, failures and edge cases. The more deployed robots a company has, the more practical experience it can feed back into its systems. Unitree Robotics has become one of the most visible Chinese robotics companies, helped by its focus on affordability and commercial delivery. Its humanoid models, including the G1 and H1, are priced at around $16,000, making them far more accessible than many competing systems. The company reportedly delivered 5,500 units to customers in 2025, with many believed to be used in research, education and small-scale industrial pilots. Lower-cost robots can accelerate experimentation across universities, labs and smaller companies. More users mean more feedback, more applications and potentially faster development. Xiaomi is best known for consumer electronics, but it has also moved into humanoid robotics with its CyberOne model. The company has highlighted features including a 4.7-billion-parameter vision, language and action model, as well as an active liquid-cooling system described as a form of robotic "sweat." CyberOne is reportedly being piloted in Chinese EV manufacturing. Humanoid robots bring together several of the most important technology trends of the moment: artificial intelligence, advanced sensors, computer vision, batteries, automation and high-precision manufacturing. The big opportunity is generalization. A robot built for one task can be useful, but a robot that can move through human environments and perform many different tasks could be transformational. The hard part is everything else. Humanoid robots must be safe, reliable, affordable and genuinely useful. They need better hands, better balance, better reasoning and better common sense. They also need business models that make sense outside carefully controlled demos. For now, factories and warehouses are likely to lead adoption because they offer structured environments and clearer returns on investment. Homes, hospitals and care settings may follow later, once robots become safer, cheaper and better at handling the unpredictable nature of everyday life. The excitement is justified, but so is caution. Humanoid robots are still in their early stages. Some companies will overpromise, some prototypes will never become products, and some business models will fail. Yet the direction of travel is clear. AI is moving from screens into the physical world, and humanoid robots are one of the most visible signs of that shift. The race is no longer about whether robots can walk across a stage or perform a carefully rehearsed trick. The real question is which companies can build robots that people and businesses actually want to use. That is where the next chapter in the evolution of technology will be written.

Американская компания Agility Robotics, один из наиболее известных разработчиков гуманоидных роботов, объявила о планах выйти на биржу через слияние со специальной компанией по приобретениям Churchill Capital Corp XI. Сделка оценивает производителя роботов примерно в $2,5 миллиарда и должна принести компании более $620 миллионов нового капитала. Из этой суммы около $200 миллионов поступят от группы новых и действующих институциональных инвесторов. Полученные средства Agility Robotics планирует направить на расширение производства следующего поколения своих гуманоидных роботов Digit v5, выполнение уже заключённых контрактов и привлечение новых заказчиков. Компания была основана в 2015 году как проект, выросший из исследований Университета штата Орегон. Наибольшую известность ей принёс робот Digit — двуногий гуманоидный робот, предназначенный для выполнения логистических и складских операций. В отличие от многих демонстрационных гуманоидных платформ, Digit уже используется в реальных коммерческих проектах. По данным компании, роботы работают на девяти площадках клиентов, включая предприятия производителя промышленных компонентов Schaeffler, логистического оператора GXO, автомобильного подразделения Toyota Motor Manufacturing Canada и крупнейшей латиноамериканской платформы электронной коммерции Mercado Libre. Agility Robotics также пользуется поддержкой ряда крупнейших технологических инвесторов. В разные годы в компанию вкладывались Amazon, Nvidia, фонд SoftBank Vision Fund 2 и венчурная компания DCVC. Дополнительный интерес инвесторов объясняется тем, что рынок гуманоидной робототехники сегодня рассматривается как одно из наиболее перспективных направлений развития искусственного интеллекта и промышленной автоматизации. Компания сообщила, что уже обеспечила многолетние заказы на новое поколение* роботов Digit v5 на сумму более $300 миллионов. Кроме того, более 30 потенциальных клиентов проводят оценку возможности внедрения гуманоидных роботов в крупных масштабах. Генеральный директор Agility Robotics Пегги Джонсон заявила, что гуманоидные роботы могут стать одним из ключевых факторов повышения производительности, устойчивости цепочек поставок и технологического лидерства США. По её словам, компании всё активнее рассматривают подобных роботов как способ компенсировать нехватку персонала, повысить эффективность производства и внедрить системы автоматизации на базе искусственного интеллекта. После завершения сделки объединённая компания будет торговаться под тикером AGLT. Конкретная североамериканская биржа, на которой состоится листинг, пока не названа. Выход Agility Robotics на публичный рынок может стать одним из наиболее заметных событий для индустрии гуманоидной робототехники, которая в последние годы привлекает всё больше инвестиций на фоне быстрого развития технологий искусственного интеллекта и автоматизации. * Деятельность организации «Духовне Управлiння Євангельських Християн Української Християнської Церкви «Нове Поколiння» (Духовное Управление Евангельских Христиан Украинской Христианской Церкви «Новое Поколение»)» признана нежелательной на территории РФ Учредитель СМИ «iXBT.com» - ООО «Аспект Исследования и Публикации». Доменное имя сайта в информационно-телекоммуникационной сети «Интернет» сетевого издания: IXBT.COM. Зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор). Регистрационный номер: Серия Эл № ФС77-91594, дата регистрации: 27 мая 2026 года. Адрес редакции: 115201, Москва, Каширское шоссе, дом 22, корп. 3, стр. 2

Американская компания Agility Robotics, один из наиболее известных разработчиков гуманоидных роботов, объявила о планах выйти на биржу через слияние со специальной компанией по приобретениям Churchill Capital Corp XI. Сделка оценивает производителя роботов примерно в $2,5 миллиарда и должна принести компании более $620 миллионов нового капитала. Из этой суммы около $200 миллионов поступят от группы новых и действующих институциональных инвесторов. Полученные средства Agility Robotics планирует направить на расширение производства следующего поколения своих гуманоидных роботов Digit v5, выполнение уже заключённых контрактов и привлечение новых заказчиков. Компания была основана в 2015 году как проект, выросший из исследований Университета штата Орегон. Наибольшую известность ей принёс робот Digit — двуногий гуманоидный робот, предназначенный для выполнения логистических и складских операций. В отличие от многих демонстрационных гуманоидных платформ, Digit уже используется в реальных коммерческих проектах. По данным компании, роботы работают на девяти площадках клиентов, включая предприятия производителя промышленных компонентов Schaeffler, логистического оператора GXO, автомобильного подразделения Toyota Motor Manufacturing Canada и крупнейшей латиноамериканской платформы электронной коммерции Mercado Libre. Agility Robotics также пользуется поддержкой ряда крупнейших технологических инвесторов. В разные годы в компанию вкладывались Amazon, Nvidia, фонд SoftBank Vision Fund 2 и венчурная компания DCVC. Дополнительный интерес инвесторов объясняется тем, что рынок гуманоидной робототехники сегодня рассматривается как одно из наиболее перспективных направлений развития искусственного интеллекта и промышленной автоматизации. Компания сообщила, что уже обеспечила многолетние заказы на новое поколение роботов Digit v5 на сумму более $300 миллионов. Кроме того, более 30 потенциальных клиентов проводят оценку возможности внедрения гуманоидных роботов в крупных масштабах. Генеральный директор Agility Robotics Пегги Джонсон заявила, что гуманоидные роботы могут стать одним из ключевых факторов повышения производительности, устойчивости цепочек поставок и технологического лидерства США. По её словам, компании всё активнее рассматривают подобных роботов как способ компенсировать нехватку персонала, повысить эффективность производства и внедрить системы автоматизации на базе искусственного интеллекта. После завершения сделки объединённая компания будет торговаться под тикером AGLT. Конкретная североамериканская биржа, на которой состоится листинг, пока не названа. Выход Agility Robotics на публичный рынок может стать одним из наиболее заметных событий для индустрии гуманоидной робототехники, которая в последние годы привлекает всё больше инвестиций на фоне быстрого развития технологий искусственного интеллекта и автоматизации. © iXBT

Agility cuenta con pedidos plurianuales por valor de más de 300 millones de dólares para su humanoide de próxima generación. El fabricante de robots humanoides Agility Robotics ha anunciado que planea salir a bolsa en una operación que valora la compañía en unos 2.500 millones de dólares. Este acuerdo convertiría a la compañía fabricante del robot Digit, en la primera empresa centrada en robots humanoides en salir a bolsa en Estados Unidos. La startup surgió de la Universidad Estatal de Oregón en 2015 y ahora está dirigida por Peggy Johnson, exdirectora ejecutiva de Magic Leap y con una larga trayectoria en Microsoft y Qualcomm. Agility planea fusionarse con Churchill Capital Corp XI, una empresa de adquisición con fines especiales (SPAC) dirigida por Michael Klein, exejecutivo de Citigroup. Se espera que cotice bajo el símbolo AGLT. "El momento es el adecuado, tanto para nuestra empresa como para el mercado", declaró Jonathan Hurst , cofundador y director de robótica de Agility, a Business Insider. "Estamos entrando ahora como pioneros, lo cual es realmente importante, y queremos ser quienes definan la industria de los robots humanoides". Una SPAC es una empresa fantasma que cotiza en bolsa, sin operaciones ni activos, salvo una importante reserva de efectivo. Se crea con el objetivo de ser adquirida o fusionada con otra empresa, lo que le permite salir a bolsa sin una oferta pública inicial tradicional. Hurst afirmó que la empresa optó por la vía de la SPAC específicamente para incorporar a Klein, cuyo equipo posee la experiencia financiera necesaria para complementar el enfoque técnico de Agility en su salida a bolsa. Klein es un prolífico promotor de SPAC y recientemente ha ayudado a empresas como Oklo, la compañía de energía nuclear respaldada por Sam Altman , y el fabricante de vehículos eléctricos Lucid a cotizar en bolsa. Se prevé que la operación genere más de 600 millones de dólares en ingresos brutos, incluyendo 420 millones en efectivo de Churchill XI y más de 200 millones mediante una inversión privada en acciones ordinarias liderada por Foxconn, el fabricante de productos electrónicos con sede en Taiwán que ya es inversor de Agility. Otros inversores de Agility incluyen la firma de capital riesgo DCVC, Nvidia, Amazon y SoftBank. Hurst afirmó que salir a bolsa permitirá a Agility ser más transparente a medida que crece y ayudará a establecer puntos de referencia en una industria que está en pleno auge. Agility afirmó que el dinero recaudado con su salida a bolsa se destinará a cumplir con los pedidos de los clientes existentes, ampliar los despliegues y aumentar la producción de su humanoide de próxima generación, Digit v5. Digit es un humanoide de tamaño real capaz de realizar tareas físicas repetitivas, como transportar cargas pesadas y clasificar mercancías. Agility ya ha desplegado su robot Digit en nueve instalaciones de clientes, entre ellas Amazon, Toyota y la empresa de logística GXO. Agility es la primera empresa en desplegar humanoides comercialmente en EE. UU. Tesla está desarrollando Optimus, cuya producción planea comenzar este verano. Boston Dynamics, propiedad de Hyundai, planea desplegar su humanoide Atlas en fábricas en 2028. Figure AI, valorada recientemente en 39.000 millones de dólares, probó sus humanoides en una planta de fabricación de BMW en Alemania y tiene un acuerdo para desplegar robots en la red de distribución y logística de Catalyst Brands, la empresa matriz de JCPenney, Aéropostale y Brooks Brothers. Agility anunció que cuenta con pedidos plurianuales para el Digit v5 por un valor superior a los 300 millones de dólares. Según Hurst, la batería del robot está diseñada para cargarse con la suficiente rapidez como para funcionar durante 20 de las 24 horas del día. Según Hurst, la principal prioridad de Agility es la seguridad. Cualquier robot humanoide suele requerir una barrera física entre él y los trabajadores humanos, y Agility quiere crear robots lo suficientemente seguros como para funcionar sin ella. "Digit v5 será el primero que podrá integrarse en entornos ya construidos sin necesidad de infraestructura adicional y realizar flujos de trabajo humanos en espacios humanos", dijo Hurst. Agility colabora con Nvidia en el sistema de seguridad. Esta semana, Nvidia anunció Halos for Robotics, un sistema de seguridad diseñado para ayudar a los robots a operar cerca de personas en entornos industriales. Agility será la primera empresa en incorporar este software a sus robots humanoides. El acuerdo de Agility con una empresa de adquisición con fines especiales (SPAC) se produce en un momento en que la industria de la robótica humanoide está pasando de las llamativas demostraciones a la implementación. El mercado está dominado por empresas chinas como Unitree, que se prepara para salir a bolsa y aspira a una valoración de hasta 7.000 millones de dólares. Según Omdia, las empresas chinas de robótica representaron cerca del 90 % de los envíos de robots humanoides el año pasado. Redactora en Business Insider España Lidia Sánchez Fernández redactora y Social Media & Content Creator en BUSINESS INSIDER ESPAÑA desde 2025. Actualmente, se especializa en temas de audiencias y fidelización, además de crear contenido para las redes sociales del medio. Queda prohibida toda reproducción sin permiso escrito de la empresa a los efectos del artículo 32.1, párrafo segundo, de la Ley de Propiedad Intelectual. Asimismo, a los efectos establecidos en el artículo 33.1 de Ley de Propiedad Intelectual, la empresa hace constar la correspondiente reserva de derechos, por sí y por medio de sus redactores o autores.

Although the adoption of humanoid robots within supply chains remains few and far between, one developer is hoping to jumpstart the technology’s expansion by going public via a Special Purpose Acquisition Company, or SPAC, merger. Agility Robotics will merge with publicly traded special purpose acquisition company Churchill Capital Corp XI, valuing the combined firm at $2.5 billion. With the transaction, Agility expects to raise more than $620 million in gross proceeds. That will include $420 million in cash held in Churchill’s trust account, alongside roughly $200 million in incremental financing led by new and existing investors led by Taiwan-based contract manufacturer Foxconn. Amazon, Nvidia and SoftBank are also among investors. Related Stories Logistics JD.com Founder Says Robots Will Eventually Replace All 700,000 Couriers, Pledges Retraining Program Industry News Textile Digitalization Accelerates as Market Demands Change Both boards of directors have unanimously approved the deal, which is expected to close by the end of the year. The firm says it will list on a major North American exchange under the ticker symbol “AGLT.” The company is most known for its Digit humanoid robot built to help warehouse and manufacturing employees with repetitive tasks like lifting boxes, stacking totes and moving items from other mobile robots onto conveyor belts. Digit robots have been used by GXO, Amazon and its Latin American e-commerce counterpart Mercado Libre. The 5-foot-9 Digit v4 robot can lift up to 35 pounds, reach 5.5 feet and tend to machines for up to 16 hours a day. The company is now preparing for the commercial launch of its next-generation humanoid robot, Digit v5, which can lift up to 50 pounds, extend its reach up to 7.2 feet and operate for as much as 22 hours per day. Beyond the numbers, the new iteration will no longer be confined to its workcell fenced off from warehouse workers, unlike previous Digit models. Instead, the v5 is built to work alongside humans throughout the facilities they operate in. Agility says it has already secured more than $300 million of multi-year orders for the v5 technology, subject to the realization of certain contractual milestones, with a growing pipeline of over 30 customers. According to Agility’s presentation on the merger Wednesday morning, Digit v5 could increase its total addressable market by improving its dexterity capabilities. For example, by the end of 2026, the firm expects the humanoids to be able to move items like structured totes and trays between two known locations with more consistent positioning. Agility intends to use the proceeds from the SPAC merger to fulfill existing customer orders, expand commercial deployments, scale production of Digit v5 and continue investing in its integrated platform spanning robotics, physical AI, software, safety systems and manufacturing infrastructure. “The demand here is large and increasing,” said Agility CEO Peggy Johnson on the investor call. “We have companies reshoring production, older workers retiring and younger generations just not opting for these types of menial jobs.” According to Agility, Digit robots have been deployed across nine customer facilities, accumulating more than 65,000 hours of operation. Last November, GXO said the humanoid moved over 100,000 totes in its facility in Flowery Branch, Ga., where the third-party logistics provider manages warehouse operations for Spanx. Amazon unveiled its first test of the Digit humanoid at a robotics R&D facility in Sumner, Wash., but has played coy over the breadth of its deployment despite its vast robotics ambitions. The companies’ relationship goes beyond partnering on robotics deployments. The company is one of the first beneficiaries of the e-commerce giant’s $1 billion venture capital vehicle, the Amazon Industrial Innovation Fund, with the tech titan injected $150 million in funding for the robotics manufacturer in 2022. With the fund, Amazon is investing in startups across the supply chain to identify and nurture technologies that address operational challenges in fulfillment centers, logistics and supply chain management. Agility wants to support the commercial scaling of the technology by keeping most of its business in-house. The company operates a cloud-based automation platform, Agility Arc, which integrates Digit into customer operations and enables deployment, fleet orchestration and operational management across multiple facilities. Agility also owns its own humanoid manufacturing facility in Salem, Ore., called “RoboFab,” which is designed to produce up to 10,000 units annually once fully up and running. The company also owns many of Digit’s hardware system, and sources about 75 percent of the robot’s parts within the U.S. Receive Our Daily Newsletter & Special Offers Receive Our Daily Newsletter & Special Offers June 26, 2026 The strike on the Ever Lovely prompted the IMO to pause its vessel evacuation process, renewing uncertainty over shipping through the conflict-ridden Strait of Hormuz. June 26, 2026 Consumers increasingly expect Amazon-like delivery speeds, forcing retailers to balance faster fulfillment with higher shipping costs, according to AlixPartners. June 25, 2026 Drewry says ocean freight rates rose to their highest level since September 2024 as importers replenish lower inventories and accelerate shipments ahead of July fuel surcharges and potential tariff changes. WWD and Women's Wear Daily are part of Penske Media Corporation. © 2026 Fairchild Publishing, LLC. All Rights Reserved.

Oops, something went wrong Agility Robotics, a US-based humanoid robotics company, has entered into a definitive agreement to merge with Churchill Capital Corp XI, a publicly traded special purpose acquisition company (SPAC). The business combination values Agility Robotics at a pre-money equity value of $2.5bn. The combined entity is expected to operate as Agility and list its shares under the ticker symbol "AGLT" on a major North American exchange. Both companies' boards of directors have unanimously approved the proposed agreement. The transaction, which remains subject to Churchill's shareholder approval, regulatory clearance, stock exchange listing authorisation, and satisfaction of other customary closing conditions, is anticipated to close in 2026. If completed, the merger is expected to provide more than $620m of gross proceeds to Agility Robotics. This includes $420m held in Churchill's trust account, assuming no redemptions. It also comprises an additional $200m of new financing through a private investment in public equity (PIPE) placement led by Foxconn at $10 per share, with participation from existing and new institutional investors. Agility Robotics, which was established in 2015 out of Oregon State University's Dynamic Robotics Laboratory, develops humanoid robots for workforce automation. The company's main product, Digit, is currently deployed across manufacturing, logistics, and distribution sites. It has been used for more than 65,000 operational hours serving enterprise customers such as Schaeffler, Toyota Motor Manufacturing Canada, GXO, and Mercado Libre. The business combination is projected to create what Agility Robotics describes as the first US publicly listed pure-play humanoid robotics firm with active commercial deployments. Churchill chairman and CEO Michael Klein said: "Churchill Capital is proud to partner with companies that are shaping the future of technology and commerce. Agility is a humanoid first mover with proven technology, real-world deployments, and the trust of some of the world's most demanding enterprises." Agility states it has secured more than $300m in multi-year orders for its next-generation Digit v5 robot, pending the achievement of certain contractual milestones, with over 30 customers identified in its pipeline. The company also notes that all current Agility shareholders will retain their equity in the combined business and be subject to a 180-day lock-up following the transaction's close. Agility, supported by investors including DCVC, Amazon, Nvidia, and SoftBank Vision Fund 2, has established a manufacturing and supply chain platform within the US, sourcing around 75% of Digit components domestically. Agility's leadership includes CEO Peggy Johnson, co-founder and chief robot officer Jonathan Hurst, and a team with a stated combined total of more than 80 years of commercial leadership and over 50 years in technical robotics. Johnson said: "Humanoid robots are a critical driver of American technology leadership and the future of global industry. "With category-defining commercially deployed humanoid robots operating in real customer environments today, Agility is at the forefront of a new era where safety-first, AI-powered technology can reliably work alongside people to bridge labour shortages, increase productivity, and strengthen the resilience of our supply chains." In April 2025, Agility announced that it is nearing completion of a $400m funding round to speed up large-scale production of its autonomous bipedal warehouse robots. "Agility Robotics to merge with Churchill Capital Corp XI in $2.5bn deal" was originally created and published by Verdict, a GlobalData owned brand.

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. Digit has logged over 65,000 hours across nine sites, generating real-world data to improve AI and robot performance. Agility Robotics is set to go public through a $2.5 billion SPAC deal, becoming the first pure-play humanoid robotics company listed in the United States. The development comes as Agility prepares to launch its next-generation Digit v5 collaborative robot. The listing is expected to generate over $620 million in funding for product development, research, and expansion, while providing investors with the first audited financial benchmark for the rapidly growing humanoid robotics sector. Recently, Agility became the first firm to integrate NVIDIA’s new Halos safety platform into its Digit humanoid robot, enhancing safe human-robot collaboration in warehouses, factories, and logistics operations. Agility Robotics is advancing humanoid automation through its Digit robot, which is now deployed in real-world manufacturing, distribution, and logistics environments with major enterprises including Schaeffler, GXO, Toyota Motor Manufacturing Canada, and Mercado Libre. The firm claims that across nine customer facilities, Digit has accumulated more than 65,000 operational hours, providing the company with extensive real-world data to refine its technology and accelerate development. Agility is now preparing for the commercial launch of Digit v5, its next-generation humanoid robot. The new model is designed as what the company describes as the world’s first “cooperatively safe” humanoid, enabling closer collaboration between robots and human workers in industrial settings. Agility Robotics plans to enhance the safety capabilities of its Digit humanoid robot by integrating NVIDIA’s IGX Thor industrial computing platform and Halos Core safety software. The technologies will be incorporated into Digit’s existing human-detection and safety systems to support safer operation alongside workers in warehouses and manufacturing facilities. Agility will also participate in NVIDIA’s Halos AI Systems Inspection Lab, where Digit’s software, computing architecture, and cybersecurity protections will be evaluated against industry safety standards. The effort aims to support large-scale, safety-certified deployment of humanoid robots. Agility claims to have already secured more than $300 million in multi-year orders for the platform and is working with a pipeline of more than 30 potential customers as enterprises explore large-scale humanoid deployment. At the core of Agility’s strategy is a data-driven approach to embodied AI. Every deployment generates proprietary operational data that helps improve Digit’s perception, decision-making, mobility, and task execution capabilities. According to Agility, the continuous feedback loop enables the robot to learn from real production environments, enhancing reliability, expanding the range of tasks it can perform, and accelerating the development of new autonomous capabilities. Agility says this growing data flywheel strengthens its AI platform while increasing value for enterprise customers. The company is also expanding its Customer Acceleration Program, which allows enterprises to evaluate humanoid robots and prepare for large-scale deployment. The initiative is helping Agility build a pipeline of future applications across multiple industries while gathering additional operational insights to improve system performance. “As adoption accelerates, we believe Agility is positioned to address a market opportunity across manufacturing, distribution, and logistics environments in the United States that is estimated by management to be approximately $1 trillion,” said Peggy Johnson, CEO of Agility Robotics, in a statement. Agility has developed the infrastructure needed to support large-scale deployment of humanoid robots across enterprise environments. At the center of this ecosystem is Agility Arc, a cloud-based automation platform that integrates Digit robots into customer operations, enabling deployment, fleet management, orchestration, and performance monitoring across multiple sites. To meet growing demand, the company has also established RoboFab, its dedicated humanoid robot manufacturing facility capable of producing up to 10,000 units annually. Agility further strengthens its operations through ownership of key hardware technologies and a domestic supply chain, with approximately 75 percent of Digit’s components sourced within the US. 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

Últimas Noticias Suscríbete al canal de Whatsapp Siempre al día de las últimas noticias Suscríbete nuestro newsletter Siempre al día de las últimas noticias VALÈNCIA. Investigadores de la Universitat Politècnica de València (UPV) lideran el proyecto Cultur-BOT-XR, cuyo objetivo es estudiar el impacto social de los robots humanoides en la vida cotidiana y, más en concreto, en la experiencia de los visitantes de museos y otros espacios culturales. “Hoy en día, apenas existen estudios que analicen el impacto de estos robots entre nosotros y en ámbitos sociales; todo se centra en el sector industrial. En este proyecto, vamos a integrar este tipo de robots en diferentes experimentos; analizaremos la reacción de la gente para tratar de vislumbrar cómo será, en un futuro no muy lejano, el trabajo compartido con humanoides”, explica Adolfo Muñoz, coordinador de Cultur-BOT-XR y codidrector del Hub de Museología Experimental (HUME) del Instituto de Diseño y Fabricación (IDF) de la UPV. En el caso de los museos, Muñoz señala que el objetivo de su equipo es ver cómo un robot humanoide puede ayudar a mostrar una exposición o interactuar con el público de otras formas. Así, el equipo de la UPV estudiará cómo pueden actuar como asistentes o mediadores culturales capaces de interactuar con los visitantes, pero también ofrecer información personalizada y adaptarse a sus necesidades e intereses. “Lo que queremos es aprovechar al máximo el potencial de las nuevas tecnologías sin perder el componente humano de la experiencia museística. Queremos saber qué tareas pueden asumir los robots para complementar el trabajo de los profesionales de museos sin sustituir el valor de la interacción personal, fundamental en la difusión cultural”, añade Ana Martí Testón, codidrectora del Hub de Museología Experimental (HUME) del Instituto de Diseño y Fabricación (IDF) y miembro también del equipo del proyecto Cultur-BOT-XR. Los investigadores de HUME-IDF están trabajando ya con Musi, el nuevo robot humanoide de la UPV. “Es el primero de los robots que hemos adquirido para este proyecto. Y será el protagonista de nuestros primeros estudios, en los que queremos ver hasta dónde va a ser aceptado en un museo y otros contextos. Musi puede ser como un embajador, una persona que te invite a explorar una colección, conocer nuevos secretos del museo. Con este proyecto, queremos analizar la interacción con el público, qué tipo de comportamientos va a tener, cómo va a reaccionar”, añade Ana Martí Testón. Desde sus laboratorios, el equipo de la UPV está trabajando también en un software propio para para crear ropa impresa específica para el robot. “Este es uno de los principales retos a los que se enfrenta esta robótica y lo que queremos es humanizarla todavía más”, apunta Adolfo Muñoz. Para el rector de la UPV, Jose E. Capilla, “la UPV tiene la responsabilidad de anticiparse a los grandes cambios tecnológicos y analizar sus implicaciones para la sociedad. Proyectos como Cultur-BOT-XR nos permiten generar conocimiento riguroso sobre una tecnología que tendrá una presencia creciente en ámbitos tan diversos como la cultura, la educación o los servicios.” “Este proyecto es -en palabras de Capilla- un ejemplo del papel que desempeñan las universidades públicas como motores de innovación. Desde la UPV impulsamos investigaciones que combinan excelencia científica, transferencia de conocimiento e impacto social, contribuyendo a que el desarrollo tecnológico responda a las necesidades reales de las personas.” El proyecto Cultur-BOT-XR está financiado por el Ministerio de Ciencia, Innovación y Universidades y la Agencia Estatal de Investigación, dentro de la convocatoria Proyectos de Generación Recibe toda la actualidad de Valencia Plaza en tu correo Suscríbete al Boletín Síguenos en redes Quiénes somos Recibe en tu correo la actualidad que te interesa De conformidad con el RGPD y la LOPDGDD, Ediciones Plaza, S.L. tratará los datos facilitados con la finalidad de remitirle noticias de actualidad de acuerdo con su perfil y preferencias sobre eventos, promociones, sorteos, concursos, cursos y otras comunicaciones comerciales. Aquí encontrarán información financiera relevante sobre nuestro negocio. Nos esforzamos por mantener la transparencia y la integridad en todas nuestras operaciones y valores. Introduce tus datos de usuario para acceder Introduce la dirección de correo electrónico con la que accedes habitualmente y te enviaremos una nueva clave de acceso.

It has happened. After months of debate over AI and robots potentially taking over human jobs, one AI company claims to have more robots than human employees at one company – Figure AI. That is, Figure AI now employs fewer humans than robots, and seems to have no plans of slowing down. On X, Figure AI CEO Brett Adcock announced this major change in his company’s workforce with a graph. He wrote, "For the first time, robots now outnumber humans at Figure.”advertisement❮❯ Read Full StoryRobots joining faster than humansThe graph, titled "Headcount vs Robots", tracks the two figures from 2022 to 2026. During this period, robot numbers remained almost flat and close to zero until the first quarter of 2025. However, as soon as the company started manufacturing robots, the number rose sharply, crossing 100 before the end of 2025 and reaching roughly 740 by the second quarter 2026. Brett Adcock shared the graph on X. The graph, titled "Headcount vs Robots", tracks the two figures from 2022 to 2026. During this period, robot numbers remained almost flat and close to zero until the first quarter of 2025. However, as soon as the company started manufacturing robots, the number rose sharply, crossing 100 before the end of 2025 and reaching roughly 740 by the second quarter 2026. Brett Adcock shared the graph on X. Human headcount, meanwhile, rose steadily from near zero in 2022 to about 400 by the end of 2025, before climbing more sharply to around 650 to 660 in 2026. That is, robots are joining Figure AI at a much faster pace than human employees. And if the chart trajectory is to be believed, there may be no signs of slowing down. In a separate post, Adcock shared an image of dozens of humanoid robots in boxes, and wrote, “Power On.” Brett Adcock shared an image of robots at Figure AI. Figure AI’s announcement comes at a time when more and more companies are moving towards automation and AI. Companies like Amazon and Meta have laid off thousands of workers as they focus more on AI. As per layoffs.fyi, roughly 119,462 workers have lost their jobs at tech companies globally so far this year. On the other hand, tech trillionaire Elon Musk has hinted that with more humanoid robots, humanity may achieve “amazing abundance for all.”What is Figure AI? Figure AI is a robotics startup that went viral a few weeks ago. The company had put up its Figure 03 robot against an intern in an 8-hour shift to sort packages in a “Man vs Machine” contest. While the intern managed to defeat the robot, Brett Adcock had warned that it was going to be the last human victory against robots. He said at the time, "This is the last time a human will ever win." Figure AI is one of many robotic startups out there. Elon Musk’s Tesla has been working on the Optimus robot for some time now, with plans to deploy it commercially in the near future. On the other hand, Chinese firms like Unitree and Agibot have also made rapid progress.- EndsPublished By: Armaan AgarwalPublished On: Jun 21, 2026 12:38 ISTAlso Read | Tesla has a new mission, amazing abundance for all after Elon Musk becomes first trillionaireAlso Read | Man 1 Machine 0: Intern defeats humanoid robot in 10-hour challengeAlso Read | AI-powered robots run like humans and shatter world records in Chinese marathon race

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. Qwen-Robot enables navigation, object handling, and world prediction, helping robots act in real environments. Chinese firm Alibaba has launched its first embodied AI model family, which links large language models with real-world robotic actions. The Qwen-Robot suite was developed by Alibaba’s Tongyi Lab and is undergoing pilot testing with selected Alibaba Cloud enterprise clients. The suite comprises three models focused on navigation, manipulation, and world modeling for robots operating in physical environments. Alibaba said the models enable machines to perceive, reason, and interact with the real world, joining a growing global push to advance embodied AI beyond traditional chatbot applications. Alibaba says its Qwen family of AI models has become very good at understanding the physical world. These models can recognize objects, understand spatial relationships, follow complex visual instructions, and reason about real-world environments. For example, a model can understand a command such as, “Go to the kitchen, find the red cup, pick it up, and place it on the shelf.” However, understanding a task is different from actually performing it. While a vision-language model (VLM) can describe the steps needed to complete a task, it cannot directly control a robot’s movements. The challenge is connecting human language and visual understanding with the motor actions required to interact with the physical world. This problem is difficult because robot training data is very different from internet data. Information collected from navigation systems, robotic arms, vehicles, and cameras comes in different formats and is expensive to gather. Simply combining all this data often creates conflicts rather than improving performance. To address this, Alibaba developed the Qwen-Robot Suite, which includes three specialized models. Qwen-RobotNav focuses on movement and navigation. It helps robots follow instructions, navigate to locations, track targets, and support autonomous driving. According to its website, Qwen-RobotManip focuses on physical interaction. It enables robots to grasp, move, and manipulate objects using a large training dataset collected from different robotic systems. Qwen-RobotWorld acts as a world model, predicting how environments may change and helping robots understand the likely outcomes of their actions. Together, these models aim to enable robots to understand instructions, interact with objects, navigate environments, and make decisions in the real world. Alibaba showcased Qwen-RobotNav on a Unitree Go2 quadruped powered by NVIDIA Jetson Thor hardware and a single low-resolution camera. The robot successfully navigated an unfamiliar apartment, following spoken instructions across multiple rooms without preloaded maps, while maintaining an inference latency of 196 milliseconds. The company claims that Qwen-RobotManip, its robotic manipulation model, was trained on more than 38,000 hours of open-source data covering object handling and interaction tasks. According to Alibaba, the model recently achieved the highest score in the generalist category of the RoboChallenge real-world robotics benchmark, earning a process score of 59.83 and a task success rate of 45 percent. The company also unveiled Qwen-RobotClaw, a robotics agent framework that enables Qwen models to use the Qwen-Robot suite as physical-world tools. In one demonstration, an agent searched for a restroom, identified an out-of-order sign, and independently rerouted to another location. Alibaba further open-sourced Chat2Robot, a browser-based platform for testing embodied AI interactions. As competition in embodied AI intensifies worldwide, Alibaba has expanded its ambitions beyond language and multimodal software with the launch of its Qwen-Robot models. The move reflects a broader industry shift toward creating AI systems capable of understanding and interacting with the physical world. Alibaba’s move comes as competition in physical AI accelerates globally. In the US, Google DeepMind is advancing Gemini Robotics, while Nvidia is expanding its robotics ecosystem through Cosmos, Isaac, and GR00T. Start-ups, including Physical Intelligence, Skild AI, and Figure AI, are also developing general-purpose robotic intelligence, according to the South China Morning Post. China is strengthening its position by pairing its manufacturing advantages with growing investments in AI software for autonomous decision-making. The sector now spans AI developers, robotics firms, and EV makers. Companies such as Alibaba, Tencent, Unitree, AgiBot, UBTech, Galbot, Spirit AI, GigaAI, Xpeng, and Xiaomi are actively pursuing embodied AI technologies. 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

Chinese tech giant Alibaba unveiled its first suite of artificial intelligence models for robots, according to a Monday (June 15) company blog post. Complete the form to unlock this article and enjoy unlimited free access to all PYMNTS content — no additional logins required. yesSubscribe to our daily newsletter, PYMNTS Today. By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. Δ The Qwen Robot Suite comes as AI companies shift away from chatbots and into physical AI. “The Qwen family of foundation models already gives strong perception and reasoning about the physical world,” the post said. “But seeing is not acting. The gap between vision and language understanding and physical control remains the central bottleneck for embodied intelligence.” The Qwen Robot Suite’s three models close this gap, per the post. Qwen-RobotNav helps robots understand how to navigate physical spaces, while Qwen-RobotWorld is a video “world model” that allows a robot to predict how a physical scenario will unfold. Lastly, Qwen-RobotManip “turns heterogeneous robot data into a coherent canonical space, enabling cross-embodiment training at scale,” the post said. “Together, they enable an agentic system where general intelligence translates directly into physical action,” the post said. The launch comes one week after a report that Alibaba Group formed a new business unit known as Token Foundry as it reorganizes to strengthen its AI efforts. Led by Alibaba CEO Eddie Wu, Token Foundry will combine Alibaba’s Tongyi Lab and Future Life business units, and operate under the company’s recently created Alibaba Token Hub. In other physical AI news, last week saw the launch of Nvidia’s Cosmos 3 foundational model for physical AI. Nvidia Founder and CEO Jensen Huang said at the launch that “the big bang of physical AI is just around the corner thanks to breakthroughs in multimodal reasoning language, vision and world models.” The distinction is important for anyone building or deploying physical AI. While a large language model learns from text, a world foundation model learns from physical environments. For robots, that means learning to handle objects with the help of millions of interaction examples, while an autonomous vehicle needs exposure to rare and dangerous scenarios that can’t be safely or cheaply collected at scale on public streets. “World foundation models solve this by generating synthetic training data that reflects real physics,” PYMNTS reported at the time. “Instead of driving a test fleet for years, an autonomous vehicle developer can run millions of simulated scenarios in days.” For all PYMNTS AI coverage, subscribe to the daily AI Newsletter. MoonPay Acquires AI Accounting Agent Maker Entendre Bilt Adds Preferred Hotels & Resorts as Transfer Partner for Members Revolut’s South Africa Waitlist Nears 100K 2 Years Before Launch Happen Sheds LendingClub Name and Launches Digital Bank Get PYMNTS Today, AI, B2B and more. We’re always on the lookout for opportunities to partner with innovators and disruptors.

24 giugno 2026 2' di lettura Translated by AI.For feedback, please contact english@ilsole24ore.com 2' di lettura Translated by AI.For feedback, please contact english@ilsole24ore.com Agility Robotics, una startup che produce robot umanoidi utilizzati negli stabilimenti di produzione e nei magazzini, è pronta a quotarsi in borsa con un’operazione che la valuta a circa 2,5 miliardi di dollari, secondo quanto riferito dai suoi dirigenti al Wall Street Journal. Le domande sono suggerite automaticamente da 24Ore AIsulla base del contenuto visualizzato. Agility è pronta a fondersi con la società di acquisizione a scopo speciale (SPAC) di Michael Klein, Churchill Capital Corp. XI, e a quotarsi in borsa con il simbolo AGLT. Le società prevedono un ricavo lordo di oltre 600 milioni di dollari dall’operazione, inclusi 420 milioni di dollari in contanti da Churchill XI e oltre 200 milioni di dollari tramite un investimento privato in azioni ordinarie (PIPE), guidato da Foxconn, il produttore a contratto di elettronica con sede a Taiwan che è già un sostenitore di Agility, hanno affermato i dirigenti. Il robot umanoide di punta di Agility è noto come Digit. I robot Digit contribuiscono ad automatizzare attività quali lo spostamento e l’impilamento di container pesanti. STRUMENTI Calcola il tuo preventivo online per l’assicurazione della tua auto, moto e motociclo PROMO ABBONAMENTO 1 anno di abbonamento al Sole a 69€!Accesso illimitato al sito de Il Sole 24 Ore Tra i concorrenti di Agility nel settore della robotica umanoide figurano aziende affermate come Tesla e Boston Dynamics, oltre a startup quali Figure AI e Apptronik. Agility è guidata da Peggy Johnson, ex dirigente di Microsoft che in precedenza è stata amministratore delegato di MagicLeap, azienda specializzata in tecnologia di realtà aumentata. Johnson ha affermato di ritenere che Agility avrà un vantaggio nel quotarsi in borsa prima di altre aziende indipendenti operanti nel settore della robotica umanoide, grazie alla domanda repressa da parte di investitori privati desiderosi di investire in questo settore. “Inoltre, riscontriamo un grande interesse da parte delle aziende che cercano di colmare la carenza di manodopera”, ha aggiunto Johnson. Johnson ha aggiunto che la combinazione tra il pensionamento dei lavoratori più anziani e l’attenzione dell’amministrazione Trump al rimpatrio dei posti di lavoro nel settore manifatturiero alimenterà la domanda dei suoi robot. Johnson ha dichiarato che lo stabilimento di Agility a Salem, nell’Oregon, dovrebbe essere in grado di produrre 10.000 unità all’anno una volta a regime. L’azienda si è già assicurata ordini per una nuova versione di Digit attualmente in fase di sviluppo, che dovrebbe avere una destrezza maggiore per movimentare oggetti più piccoli e incorporare standard di sicurezza più elevati, secondo quanto dichiarato da Johnson. Tra gli altri finanziatori di Agility figurano Amazon, Nvidia e SoftBank. Ieri, 23 giugno, Nvidia ha annunciato un nuovo protocollo di sicurezza per la robotica che, secondo quanto dichiarato, Agility sarà la prima azienda a implementare. Notizie e approfondimenti sugli avvenimenti politici, economici e finanziari. App disponibile su:Google Play App store P.I. 00777910159 Dati societari © Copyright Il Sole 24 Ore Tutti i diritti riservati Per la tua pubblicità sul sito: 24 Ore System Informativa sui cookie Privacy policy Accessibilità TDM Disclaimer

中央訊息 （中央社記者江明晏台北24日電）美國機器人公司Agility Robotics宣布將在美國那斯達克掛牌上市，去年能率亞洲及佳能共投資1000萬美元，配合未來掛牌增資，能率集團預計將再投資約1000萬美元；佳能指出，與Agility合作開發的人形機器人視覺模組，預計年底前開始貢獻業績。 全球人形機器人掀起熱潮，繼中國宇樹科技籌備上市後，根據華爾街日報報導，美國Agility Robotics也宣布將透過SPAC（特殊目的收購公司）的方式在美國納斯達克掛牌上市。分析師指出，中國跟美國人形機器人的上市籌資大戰將正式引爆；業界看好，人形機器人的發展與量產將會加速，台灣機器人相關廠商將迎來商機。 Agility robotics是美國首家股票上市的人形機器人公司，股東陣容除了經營團隊以外，包括亞馬遜、輝達、軟銀、Sony及台灣能率集團佳能與能率亞洲等。 能率集團指出，去年能率亞洲及佳能投資Agility robotics共1000萬美元，約新台幣3.2億元；配合未來掛牌增資，能率亞洲、佳能及首次投資的能率創新預計再增加投資約1000萬美元，累計總投資金額會達到2000萬美元，約新台幣6.4億元。 能率指出，目前Agility Robotics正處於關鍵發展期，主力商品Digit V4機器人已在物流工作流程的實際客戶環境中運行，從試點轉向規模化部署，預計Digit V5機器人將於2026年完成最終開發，並計劃2027年規模化生產擴大部署並降低成本。 Agility統計，至2025年底Digit機器人在大型客戶（如GXO物流、亞馬遜 Amazon）真實電商倉儲環境中，累計搬運超過10萬個貨箱。 近期Agility與輝達也宣布深化合作，除了Digit機器人全面導入輝達的Cosmos與Isaac Lab技術，利用輝達GPU加速模擬環境，可大幅縮短控制策略訓練時間。輝達並在美國時間22日舉辦的Automate 2026機器人大展，與Agility Robotics共同發表新一代Digit導入NVIDIA Halos安全框架。 另外，Agility Robotics日前與豐田汽車加拿大製造廠（TMMC）合作，在經歷3台Digit的初期測試後，與Agility簽署「機器人即服務（RaaS）」合約，擴大部署至10台。 而在台灣廠商方面，Agility創辦人Jonathan Hurst於去年9月親自拜訪佳能進行密切洽商，雙方正深化合作開發與量產人形機器人視覺模組。隨著未來Agiltiy股票上市，加上Agility V5將於明年生產並陸續放量，佳能預計年底前有機會開始貢獻業績。 除Agility之外，佳能表示，生產的機器人感測模組給Mantis Robotics已從去年第4季開始陸續出貨；佳能也看好未來無人機，AI監視系統，機器人等的需求，下半年營收將持續成長。（編輯：張良知）1150624 選擇與事實站在一起，您的每一份贊助，都是守護新聞自由的力量 下載中央社「一手新聞」APP，即時掌握最新消息 本網站之文字、圖片及影音，非經授權，不得轉載、公開播送或公開傳輸及利用。