AUTOMATION WORKFLOW

Humanoid robots emerged as one of the most talked about technologies at a major Silicon Valley summit this week, signaling how quickly machines designed to move and interact like humans are moving from experimental labs into real world applications. At the event, technology companies, robotics startups, and artificial intelligence researchers demonstrated humanoid robots capable of walking, grasping objects, responding to voice commands, and navigating complex environments. These demonstrations underscored how advances in AI models, sensors, and mechanical design are converging to accelerate the development of robots that can operate in spaces built for people. Industry leaders at the summit said humanoid robots represent a critical next step in automation. Unlike traditional industrial robots that work in controlled factory settings, humanoid robots are designed to function in homes, hospitals, warehouses, and offices without requiring major infrastructure changes. This flexibility could make them suitable for tasks ranging from elder care and logistics to manufacturing support and disaster response. Several speakers highlighted how recent progress in large language models and computer vision has dramatically improved robots’ ability to understand instructions and adapt to unfamiliar situations. Instead of following rigid programming, newer humanoid systems can learn from observation, interpret spoken language, and make decisions in real time. Researchers noted that this shift brings robots closer to being general purpose assistants rather than single task machines. However, experts at the summit also acknowledged significant challenges ahead. Power efficiency, safety, affordability, and reliability remain major hurdles before humanoid robots can be deployed at scale. There are also ongoing debates about ethical considerations, workforce displacement, and how societies should regulate machines that closely mimic human behavior. As AI systems become more capable, companies are increasingly looking to give intelligence a physical form. While widespread adoption may still be years away, the momentum on display suggests humanoid robots are no longer a distant concept but an emerging reality that could reshape how humans work and live. Abdul Wasay explores emerging trends across AI, cybersecurity, startups and social media platforms in a way anyone can easily follow. Apple approves a driver that enables Nvidia eGPUs on Arm Macs, marking a shift in GPU support for Apple Silicon devices. A major EU data breach exposed emails, user data, and internal records after hackers accessed cloud systems and leaked files online. Large-scale theft has hit the Sukkur to Multan section of the M5 Motorway, where multiple high-tech surveillance and speed cameras have gone missing across nearly. China has officially moved up the delivery timeline of its J-35 stealth fighters to Pakistan. Initially set for late 2026, the advanced aircraft will now. Premier Pakistan technology news website with special focus on startups, entrepreneurship and consumer products. © 2026 TechJuice.PK – All rights reserved.

From daily news and career tips to monthly insights on AI, sustainability, Aerospace, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies. We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. The robot uses artificial sweating to cool the powerful motors. Xiaomi has unveiled a major redesign of its CyberOne humanoid robot, introducing a new full-palm tactile bionic hand. It combines high-density sensing, improved dexterity, and an unusual liquid cooling system inspired by human sweating. The update was detailed through Xiaomi Technology’s official WeChat account, where the company outlined how the new hand design moves closer to human-scale manipulation and long-duration industrial operation. The redesigned hand is significantly smaller than the previous version, with Xiaomi reducing the hand’s volume by 60 percent to achieve a 1:1 human scale. The dimensions are based on a 1.73-meter (5.6 feet) human hand model, which the company says helps improve sim-to-real transfer when training robotic manipulation systems in simulation before deploying them in the real world. The new bionic hand also introduces a major increase in dexterity. Xiaomi said the configuration increases active degrees of freedom by 83 percent, bringing the robot’s bionic hand closer to the human hand standard of roughly 22 to 27 degrees of freedom required for complex manipulation tasks. A key part of the redesign is full-palm tactile sensing. The sensing area reportedly covers around 8,200 square millimeters, allowing the robot to detect pressure and contact across the entire palm rather than just the fingertips. This is significant because many robotic hands rely primarily on vision systems and fingertip sensors. Full-palm tactile sensing allows the robot to continue manipulating objects even when cameras are obstructed or when precise force control is required, such as in assembly tasks. Xiaomi also reported durability improvements, with the hand surviving more than 150,000 grasping cycles, which is substantially higher than the roughly 10,000-cycle failure threshold commonly seen in tendon-driven robotic hands. One of the most unusual features of the new CyberOne hand is its liquid cooling system, designed to address overheating in high-density motors used in dexterous robotic hands. According to Xiaomi, the hand’s compact motors can generate significant heat during continuous operation. To manage this, the company integrated 3D-printed metal liquid cooling channels inside the hand that function similarly to sweat glands. Thermal management is a major challenge in humanoid robotics, particularly for robotic hands, which must pack multiple motors, sensors, and transmission systems into a very small space. Overheating can reduce motor performance, shorten component lifespan, and limit continuous operation time. Xiaomi also shared early industrial testing results for the new hand. In automotive assembly tests, CyberOne reportedly achieved a 90.2 percent success rate for nut-fastening tasks within a strict 76-second factory cycle over three hours of operation. To support broader research in robotic manipulation and embodied AI, Xiaomi said it used tactile gloves for direct data collection and has open-sourced the TacRefineNet framework along with 61 hours of raw tactile data. The company suggests that combining full-palm tactile sensing with active liquid cooling could help enable humanoid robots to operate continuously in industrial environments, where dexterity, reliability, and thermal stability are critical for deployment. Atharva is a full-time content writer with a post-graduate degree in media & amp; entertainment and a graduate degree in electronics & telecommunications. He has written in the sports and technology domains respectively. In his leisure time, Atharva loves learning about digital marketing and watching soccer matches. His main goal behind joining Interesting Engineering is to learn more about how the recent technological advancements are helping human beings on both societal and individual levels in their daily lives. 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From daily news and career tips to monthly insights on AI, sustainability, Aerospace, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies. We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. Figure AI’s humanoid robot walks beside Shawn Ryan in a real-world demo. In a recent episode of the Shawn Ryan Show, host Shawn Ryan came face-to-face with something that until recently belonged mostly to science fiction. The former U.S. Navy SEAL and CIA contractor walked alongside a fully functioning AI-Powered humanoid robot. The machine, Figure 03, developed by robotics startup Figure AI, is designed to perform many of the same tasks humans do, from folding laundry and washing dishes to working in factories and logistics centers. During the walkthrough demonstration with Figure AI founder and CEO Brett Adcock, Ryan interacted directly with the robot, testing its balance, movement, and responsiveness. The brief tour followed a much longer interview on the show, during which Adcock explained how his company is racing to build general-purpose humanoid robots that could eventually become commonplace in workplaces and possibly homes. The short demonstration video shows the Figure 03 robot walking beside Ryan, guided entirely by AI. According to Adcock, the robot stands about 5 feet 6 inches tall and weighs roughly 130-135 pounds, placing it close to human proportions. Unlike earlier robotics systems that relied heavily on scripted movements, the robot’s locomotion and actions are controlled through a neural network. As Adcock explained during the demo, the walking motion is generated by AI rather than traditional coded instructions. The robot contains around 40 joints, powered by electric motors equipped with sensors that help it maintain balance and perform tasks. Ryan, impressed by the light, foam-like exterior, questioned the robot’s durability and its ability to recover if it fell. Fall recovery, being an essential feature for robots operating in real-world environments, is a critical part of any humanoid evaluation. And while Figure is trained in simulation for dynamic stability, strength, and coordination, Addcock remarked that it totally depends on how the body falls, and that sometimes they even end up breaking necks. Another feature highlighted in the walkthrough is the robot’s hands. Cameras embedded in the palms help the machine visually track objects as it grasps them, while tactile sensors in every fingertip measure pressure during contact. This combination enables the robot to perform dexterous tasks. According to Adcock, Figure’s machines can lift boxes weighing up to 40 pounds and even fold a T-shirt. During the demonstration, Ryan jokingly asked whether the robot could crush his hand when shaking it. Adcock reassured him that the machine’s force control prevents such scenarios. While the demonstration showcased the robot’s movement and interaction, the podcast’s longer conversation focused on Figure AI’s broader ambitions. Founded in 2022, the company aims to develop general-purpose humanoid robots capable of replacing or assisting human labor in industries facing worker shortages. Adcock said early deployments are focused on commercial environments such as manufacturing and logistics. The company already works with several major partners, including BMW, where the robots are being tested in manufacturing settings. Figure is also collaborating with large logistics and real estate organizations to evaluate how humanoid robots could integrate into industrial workflows. Inside the robot’s torso sits most of its computing hardware, including GPUs and battery systems that power the machine. According to Adcock, a fully charged robot can operate for about four to five hours, after which it can recharge in roughly one hour. One unusual design feature is the charging system. Instead of plugging in cables, the robot charges wirelessly through pads embedded in its feet, allowing it to replenish energy simply by standing on a charging mat. Adcock compared the development of humanoid robots to the early years of smartphones, predicting rapid improvements with each generation of hardware. “This will look like the iPhone lineup,” he told Ryan, suggesting each new version will bring major improvements in capability. Figure AI’s ambitions go beyond building a handful of demonstration machines. According to Adcock, the company has already set up a manufacturing facility capable of producing robots on an increasing scale. When the production line is running, the factory can currently assemble one robot roughly every 90 minutes. In the long term, the company hopes to dramatically increase that output. He suggested that humanoid robots could eventually reach production levels comparable to consumer electronics, potentially reaching millions of units per year. The ultimate goal, he added, is a future where robots become as ubiquitous as smartphones, possibly even approaching a “robot for every human.” Humanoids are increasingly appearing outside the lab. Last week, a Figure humanoid robot made an appearance at a White House event focused on artificial intelligence, greeting attendees and demonstrating its capabilities. The widely publicized moment signaled how quickly humanoid robotics is moving from experimental prototypes into the public spotlight. The technology is increasingly entering mainstream discussion. Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder. Exclusive content, expert insights and a deeper dive into engineering and tech. No ads, no limits. Exclusive content, expert insights and a deeper dive into engineering and tech. No ads, no limits. Premium Follow

Startup, który w zawrotnym tempie goni marzenia o robotycznej rewolucji, znów udowodnił, że ich humanoidy są już gotowe, by wyjść z laboratoriów prosto do fabryk, a w przyszłości także do naszych domów. Największym zaskoczeniem podczas demonstracji, którą poprowadził założyciel firmy Brett Adcock, był sposób poruszania się robota. W przeciwieństwie do maszyn starszej generacji, które poruszały się według sztywno zaprogramowanych skryptów, Figure 03 opiera się na „ruchach generowanych przez AI”. Oznacza to, że za każdy krok, uścisk dłoni czy stabilizację sylwetki odpowiada sieć neuronowa, a nie linijki tradycyjnego kodu. Robot o wzroście około 168 cm i wadze blisko 60 kg posiada proporcje zbliżone do ludzkich, co pozwala mu na operowanie w środowiskach zaprojektowanych dla nas. Shawn Ryan, testując responsywność maszyny, zwrócił uwagę na jej delikatne, piankowe wykończenie i zapytał o trwałość. Adcock szczerze przyznał, że choć roboty są trenowane w zaawansowanych symulacjach, upadki w realnym świecie wciąż bywają ryzykowne – czasem kończą się nawet „skręceniem karku”. Niemniej jednak, postęp w koordynacji ruchowej jest kolosalny. Figure 03 posiada aż 40 stawów napędzanych silnikami elektrycznymi, a jego dłonie to majstersztyk inżynierii: Robot jest w stanie podnosić skrzynie o wadze do 18 kg, co czyni go idealnym kandydatem do pracy w centrach logistycznych. Co ciekawe, system ładowania jest całkowicie bezprzewodowy – robot uzupełnia energię (która starcza na 4-5 godzin pracy), po prostu stając na specjalnej macie ładującej. Dobrze już wiemy, że firmy stojące za robotami, nie chcą ograniczać się tylko do prezentacji, nawet tych najbardziej widowiskowych. Adcock porównuje obecny etap rozwoju humanoidów do wczesnych lat smartfonów. Przewiduje, że każda kolejna generacja (podobnie jak kolejne modele iPhone’a) będzie przynosić skokową poprawę możliwości. Firma nie buduje już prototypów w garażu – posiada w pełni funkcjonalną fabrykę, która obecnie jest w stanie złożyć jednego robota w około 90 minut. Czytaj też: Robot, który obiera jabłka. Sharpa uczy maszyny ludzkiej zręczności Docelowo startup chce produkować miliony jednostek rocznie, dążąc do wizji „robota dla każdego człowieka”. Już teraz maszyny od Figure AI przechodzą testy w zakładach BMW, gdzie sprawdzają się w trudnych warunkach produkcyjnych. O tym, jak blisko mainstreamu jest ta technologia, świadczy fakt, że niedawno jeden z robotów Figure pojawił się w Białym Domu, witając gości podczas wydarzenia poświęconego sztucznej inteligencji. Przejście od eksperymentu do oficjalnych państwowych prezentacji zajęło firmie zaledwie cztery lata, a to imponujące. Oczywiście do spełnienia ambitnych celów wciąż daleka droga, ale jeśli do tego dojdzie, to być może za kilka lat roboty przestaną być ciekawostką, a staną się codziennością. Źródło: Shawn Ryan Show Portal technologiczny z ponad 29-letnią historią, piszący o nauce i technice, smartfonach, motoryzacji, fotografii. Technologie mamy we krwi!

Si votre email correspond à un compte, vous recevrez un lien de réinitialisation. Le secteur des robots humanoïdes, soutenu par des milliards d'investissements, ferait face à un risque de bulle selon des figures éminentes comme Rodney Brooks (cofondateur d'iRobot) et Yann LeCun (chef scientifique IA de Meta). Ils pointent l'incapacité des approches actuelles, notamment en matière de dextérité et d'intelligence générale, à justifier les promesses faites par des entreprises comme Tesla et Figure. La course pour développer des robots humanoïdes autonomes et polyvalents est en plein essor. Des sociétés comme Figure, récemment valorisée à un niveau "étonnant" de 39 milliards de dollars après une levée de fonds dépassant le milliard, ou encore Tesla avec son projet Optimus, nourrissent des ambitions démesurées. Le PDG de Figure, Mike Cagney, et Elon Musk, promettent un impact économique significatif d'ici cinq ans. Cependant, deux des esprits les plus respectés du domaine, le roboticien Rodney Brooks et le scientifique en chef de l'IA chez Meta, Yann LeCun, viennent de jeter une ombre sur cet optimisme financier. Ils estiment que nous sommes dans la phase initiale du cycle de battage médiatique (ou cycle de la hype) pour les humanoïdes, juste au moment où l'intelligence artificielle générale commence à descendre de son pic. Cette dichotomie entre l'optimisme financier et les réalités technologiques est au cœur de leur mise en garde. Rodney Brooks, roboticien de renom ayant passé des décennies au MIT, a co-écrit un article expliquant "Pourquoi les humanoïdes d'aujourd'hui n'apprendront pas la dextérité". Son constat est sans appel : les centaines de millions, voire les milliards, de dollars investis par les capitaux-risqueurs et les grandes entreprises technologiques pour leur entraînement sont dépensés pour une approche qui ne peut pas aboutir. Pour lui, croire qu'une dextérité humaine sera atteinte dans les décennies à venir est "de la pure fantaisie". Le cœur du problème réside dans les mains. Les mains humaines disposent d'environ 17 000 récepteurs tactiles spécialisés. Selon Brooks, aucune technologie robotique actuelle n'est proche de cette capacité. Alors que l'apprentissage automatique a transformé la reconnaissance vocale et le traitement d'image grâce à des décennies de données spécifiques, il n'existe pas de "tradition" équivalente pour les données de toucher dont les robots auraient besoin. Les tentatives de certaines entreprises, comme Figure ou Tesla, d'enseigner la dextérité aux robots en leur montrant des vidéos d'humains accomplissant des tâches sont particulièrement visées par le cofondateur d'iRobot. Il souligne que les efforts pour construire des mains de type humain, même s'ils existent depuis des décennies, n'ont pas encore résolu ce goulot d'étranglement fondamental lié à l'acquisition de données sensorielles riches. De son côté, Yann LeCun, lauréat du prix Turing et pionnier du deep learning, pointe du doigt l'intelligence même de ces machines. Le chef scientifique de Meta a averti lors du symposium inaugural de l'Impact de l'IA Générative du MIT que le "grand secret de l'industrie" est qu'aucune de ces entreprises n'a la moindre idée de la manière de rendre ces robots suffisamment intelligents pour être "généralement utiles". Il précise que si des robots peuvent être entraînés pour des tâches spécifiques, comme dans le domaine manufacturier, le robot domestique nécessitera des percées majeures en IA. LeCun estime que les grands modèles de langage (LLM) actuels ne sont pas la solution. Il rappelle qu'un enfant de quatre ans a emmagasiné autant de données visuelles "à haut débit" que le plus grand des LLM sur le texte public, soulignant que "nous n'atteindrons jamais l'intelligence de niveau humain en nous entraînant uniquement sur du texte". Pour sortir de cette impasse, l'avenir réside dans ce qu'on appelle les modèles du monde (world models). Ces systèmes IA apprennent à comprendre le monde physique à partir de données sensorielles (vidéo). L'objectif est de prédire l'état futur du monde après une action imaginée par l'agent. LeCun, qui mène des recherches sur des architectures comme le V-JEPA, est convaincu que ces modèles sont la clé pour que les robots accomplissent des tâches "sans entraînement" (zero shot). Au-delà de l'intelligence et de la dextérité, Rodney Brooks soulève un point souvent négligé : la sécurité. Les robots humanoïdes bipèdes, en raison de l'énergie massive qu'ils doivent déployer pour rester debout et marcher, représentent un danger non négligeable en cas de chute. Cette problématique physique s'ajoute aux défis logiciels, incitant Brooks à prédire que dans une quinzaine d'années, les humanoïdes qui réussiront ressembleront peu aux modèles anthropomorphes actuels. Ils seront probablement dotés de roues, de multiples bras et de capteurs spécialisés, abandonnant la forme humaine pour des raisons d'efficacité. L'alerte lancée par Brooks et LeCun force l'industrie à se poser la question fondamentale : le financement massif d'expériences d'entraînement coûteuses peut-il réellement conduire à une production de masse évolutive sans adresser d'abord les goulots d'étranglement de l'IA fondamentale ? Le débat fait rage, et l'échéance des cinq prochaines années fixée par certains entrepreneurs servira de juge de paix sur la viabilité de la forme humanoïde actuelle. 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Drones have gained traction as a versatile form of assistive robots for Blind and Low Vision (BLV) people. Nonetheless, novel interaction techniques are required to enable BLV people to communicate with drones naturally. In this work, we built an LLM-powered assistive drone for BLV users. We leverage an LLM to translate high-level user goals to step-by-step instructions for the drone and to extract visual information from the images. Through a formative study with BLV users (N=9), we identified envisioned use cases and desired interaction modalities. Then, we took a participatory and iterative approach to build a prototype, incorporating feedback received from 3 BLV users, as well as 5 domain experts. Finally, we conducted a user study with an additional 6 BLV participants to evaluate the iterated prototype, and received positive feedback. This work is contributing to a growing body of research on harnessing the power of LLMs to build a more inclusive world. Drones have gained traction as a versatile form of assistive robots for Blind and Low Vision (BLV) people. Nonetheless, novel interaction techniques are required to enable BLV people to communicate with drones naturally. In this work, we built an LLM-powered assistive drone for BLV users. We leverage an LLM to translate high-level user goals to step-by-step instructions for the drone and to extract visual information from the images. Through a formative study with BLV users (N=9), we identified envisioned use cases and desired interaction modalities. Then, we took a participatory and iterative approach to build a prototype, incorporating feedback received from 3 BLV users, as well as 5 domain experts. Finally, we conducted a user study with an additional 6 BLV participants to evaluate the iterated prototype, and received positive feedback. This work is contributing to a growing body of research on harnessing the power of LLMs to build a more inclusive world. Connectez-vous pour contacter le contributeur https://hal.science/hal-05567674 Soumis le : jeudi 26 mars 2026-08:38:42 Dernière modification le : lundi 30 mars 2026-12:48:20 Contact Ressources Informations Questions juridiques Portails CCSD

Tesla removes Model S and X custom orders as sunset officially begins SpaceX files confidentially for IPO that will rewrite the record books Elon Musk hints at “official ceremony” with throwback photo to close Tesla Model S, Model X chapter Elon Musk announces disappointing Tesla Optimus update Countdown: America is going back to the Moon and SpaceX holds the key to what comes after Tesla removes Model S and X custom orders as sunset officially begins Elon Musk hints at “official ceremony” with throwback photo to close Tesla Model S, Model X chapter Elon Musk announces disappointing Tesla Optimus update Musk forces Judge’s exit from shareholder battles over viral social media slip-up Tesla FSD mocks BMW human driver: Saves pedestrian from near miss SpaceX files confidentially for IPO that will rewrite the record books Countdown: America is going back to the Moon and SpaceX holds the key to what comes after Elon Musk debunks latest rumors about SpaceX IPO Tesla and SpaceX to merge in 2027, Wall Street analyst predicts TIME honors SpaceX’s Gwynne Shotwell: From employee No. 7 to world’s most valuable company SpaceX files confidentially for IPO that will rewrite the record books Elon Musk hints at “official ceremony” with throwback photo to close Tesla Model S, Model X chapter Elon Musk announces disappointing Tesla Optimus update Countdown: America is going back to the Moon and SpaceX holds the key to what comes after Elon Musk debunks latest rumors about SpaceX IPO In a post on X on March 31, Musk stated that Optimus 3 is mobile but requires some finishing touches before it is ready to be shown to the world. This update comes on the final day of the first quarter, a period when Tesla had previously signaled expectations for a Gen 3 reveal. Published on By Elon Musk announced a disappointing update to the unveiling of Tesla Optimus and its third-generation iteration, missing a timeline it aimed to hit in the first quarter of the year. Musk has confirmed that the highly anticipated Optimus Gen 3 humanoid robot is already walking around and operational, yet the public unveiling will face a short delay as the company applies final refinements. In a post on X on March 31, Musk stated that Optimus 3 is mobile but requires some finishing touches before it is ready to be shown to the world. This update comes on the final day of the first quarter, a period when Tesla had previously signaled expectations for a Gen 3 reveal. Optimus 3 is walking around, but needs some finishing touches before it’s ready to be shown — Elon Musk (@elonmusk) March 31, 2026Advertisement The announcement follows reports of Optimus Gen 3 appearing at the Tesla Diner in Los Angeles, where it was observed serving and moving about until sunset. Images and videos shared by observers captured the robot in action, highlighting its progress in real-world mobility. Tesla had aimed to showcase the production intent version of Optimus Gen 3 during the first quarter of 2026, positioning it as a major step toward factory deployment and eventual commercial availability. Musk has described the robot as featuring advanced capabilities, including highly dexterous hands with significant degrees of freedom, powered by Tesla’s AI systems for complex tasks. This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Musk has confirmed that the highly anticipated Optimus Gen 3 humanoid robot is already walking around and operational, yet the public unveiling will face a short delay as the company applies final refinements. In a post on X on March 31, Musk stated that Optimus 3 is mobile but requires some finishing touches before it is ready to be shown to the world. This update comes on the final day of the first quarter, a period when Tesla had previously signaled expectations for a Gen 3 reveal. Optimus 3 is walking around, but needs some finishing touches before it’s ready to be shown — Elon Musk (@elonmusk) March 31, 2026Advertisement The announcement follows reports of Optimus Gen 3 appearing at the Tesla Diner in Los Angeles, where it was observed serving and moving about until sunset. Images and videos shared by observers captured the robot in action, highlighting its progress in real-world mobility. Tesla had aimed to showcase the production intent version of Optimus Gen 3 during the first quarter of 2026, positioning it as a major step toward factory deployment and eventual commercial availability. Musk has described the robot as featuring advanced capabilities, including highly dexterous hands with significant degrees of freedom, powered by Tesla’s AI systems for complex tasks. This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement In a post on X on March 31, Musk stated that Optimus 3 is mobile but requires some finishing touches before it is ready to be shown to the world. This update comes on the final day of the first quarter, a period when Tesla had previously signaled expectations for a Gen 3 reveal. Optimus 3 is walking around, but needs some finishing touches before it’s ready to be shown — Elon Musk (@elonmusk) March 31, 2026Advertisement The announcement follows reports of Optimus Gen 3 appearing at the Tesla Diner in Los Angeles, where it was observed serving and moving about until sunset. Images and videos shared by observers captured the robot in action, highlighting its progress in real-world mobility. Tesla had aimed to showcase the production intent version of Optimus Gen 3 during the first quarter of 2026, positioning it as a major step toward factory deployment and eventual commercial availability. Musk has described the robot as featuring advanced capabilities, including highly dexterous hands with significant degrees of freedom, powered by Tesla’s AI systems for complex tasks. This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Optimus 3 is walking around, but needs some finishing touches before it’s ready to be shown — Elon Musk (@elonmusk) March 31, 2026Advertisement — Elon Musk (@elonmusk) March 31, 2026Advertisement The announcement follows reports of Optimus Gen 3 appearing at the Tesla Diner in Los Angeles, where it was observed serving and moving about until sunset. Images and videos shared by observers captured the robot in action, highlighting its progress in real-world mobility. Tesla had aimed to showcase the production intent version of Optimus Gen 3 during the first quarter of 2026, positioning it as a major step toward factory deployment and eventual commercial availability. Musk has described the robot as featuring advanced capabilities, including highly dexterous hands with significant degrees of freedom, powered by Tesla’s AI systems for complex tasks. This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement The announcement follows reports of Optimus Gen 3 appearing at the Tesla Diner in Los Angeles, where it was observed serving and moving about until sunset. Images and videos shared by observers captured the robot in action, highlighting its progress in real-world mobility. Tesla had aimed to showcase the production intent version of Optimus Gen 3 during the first quarter of 2026, positioning it as a major step toward factory deployment and eventual commercial availability. Musk has described the robot as featuring advanced capabilities, including highly dexterous hands with significant degrees of freedom, powered by Tesla’s AI systems for complex tasks. This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Tesla had aimed to showcase the production intent version of Optimus Gen 3 during the first quarter of 2026, positioning it as a major step toward factory deployment and eventual commercial availability. Musk has described the robot as featuring advanced capabilities, including highly dexterous hands with significant degrees of freedom, powered by Tesla’s AI systems for complex tasks. This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement This minor postponement aligns with Tesla’s iterative approach to development. Earlier statements from Musk indicated that Gen 3 would represent the most advanced humanoid robot yet, designed primarily for internal factory use before scaling to external customers.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Production timelines point toward low-volume output starting in the summer of 2026, with volume ramp-up targeted for 2027. The delay underscores the company’s commitment to quality over speed, ensuring the robot meets rigorous standards for safety and performance in practical environments. Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Optimus represents a cornerstone of Tesla’s long-term vision beyond electric vehicles. Musk has repeatedly emphasized that successful humanoid robotics could transform industries by addressing labor shortages and enabling new forms of productivity. Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Competitors in the space continue to advance their own platforms, yet Tesla’s vertical integration, from custom actuators to end-to-end AI training, positions Optimus as a potential leader. Community reactions on social media range from excitement over visible progress to impatience with shifting timelines, a familiar pattern in Tesla’s innovation journey.Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Investors and enthusiasts view Optimus as critical to Tesla’s valuation, potentially surpassing its automotive business in scale. With the robot already demonstrating walking and basic interactions, the finishing touches likely involve software polishing, hardware fine-tuning, and reliability enhancements. Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement Musk’s update suggests the reveal could arrive in the coming weeks or months, maintaining momentum toward broader deployment. As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. Advertisement As Tesla pushes the boundaries of physical artificial intelligence, this latest development keeps Optimus in the spotlight. The company continues to prioritize rapid iteration while delivering on its promises to shareholders and customers. The robotics revolution at Tesla appears closer than ever, promising profound impacts on manufacturing, services, and daily life in the years ahead. 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 SpaceX files confidentially for a record-breaking IPO targeting a $1.75T valuation and $80B raise, driven by Starlink growth and its xAI merger. Published on By Elon Musk’s rocket and satellite company submitted its draft registration to the U.S. Securities and Exchange Commission today for an initial public offering, targeting June at a $1.75 trillion valuation. This would be the largest in history. SpaceX has filed confidentially with the SEC, first reported by Bloomberg. SpaceX would be valued above every S&P 500 company except Nvidia, Apple, Alphabet, Microsoft, and Amazon. The filing uses a confidential process that allows companies to work through SEC disclosures privately before initiating a public roadshow. With a June target, official details through a formal prospectus is expected to go public in April or early May, after which SpaceX must wait at least 15 days before beginning investor marketing. SpaceX IPO is coming, CEO Elon Musk confirms Advertisement While SpaceX is best known for its Falcon 9 and Starship rockets, the $1.75 trillion valuation is anchored by Starlink, its satellite internet service. Starlink ended 2025 with 9.2 million subscribers and over $10 billion in revenue, which is a figure analysts project could reach a staggering $24 billion by the end of 2026. A February all-stock merger with xAI, Musk’s artificial intelligence venture, further boosted the valuation. SpaceX officially acquires xAI, merging rockets with AI expertise Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement SpaceX has filed confidentially with the SEC, first reported by Bloomberg. SpaceX would be valued above every S&P 500 company except Nvidia, Apple, Alphabet, Microsoft, and Amazon. The filing uses a confidential process that allows companies to work through SEC disclosures privately before initiating a public roadshow. With a June target, official details through a formal prospectus is expected to go public in April or early May, after which SpaceX must wait at least 15 days before beginning investor marketing. SpaceX IPO is coming, CEO Elon Musk confirms Advertisement While SpaceX is best known for its Falcon 9 and Starship rockets, the $1.75 trillion valuation is anchored by Starlink, its satellite internet service. Starlink ended 2025 with 9.2 million subscribers and over $10 billion in revenue, which is a figure analysts project could reach a staggering $24 billion by the end of 2026. A February all-stock merger with xAI, Musk’s artificial intelligence venture, further boosted the valuation. SpaceX officially acquires xAI, merging rockets with AI expertise Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement The filing uses a confidential process that allows companies to work through SEC disclosures privately before initiating a public roadshow. With a June target, official details through a formal prospectus is expected to go public in April or early May, after which SpaceX must wait at least 15 days before beginning investor marketing. SpaceX IPO is coming, CEO Elon Musk confirms Advertisement While SpaceX is best known for its Falcon 9 and Starship rockets, the $1.75 trillion valuation is anchored by Starlink, its satellite internet service. Starlink ended 2025 with 9.2 million subscribers and over $10 billion in revenue, which is a figure analysts project could reach a staggering $24 billion by the end of 2026. A February all-stock merger with xAI, Musk’s artificial intelligence venture, further boosted the valuation. SpaceX officially acquires xAI, merging rockets with AI expertise Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement SpaceX IPO is coming, CEO Elon Musk confirms Advertisement While SpaceX is best known for its Falcon 9 and Starship rockets, the $1.75 trillion valuation is anchored by Starlink, its satellite internet service. Starlink ended 2025 with 9.2 million subscribers and over $10 billion in revenue, which is a figure analysts project could reach a staggering $24 billion by the end of 2026. A February all-stock merger with xAI, Musk’s artificial intelligence venture, further boosted the valuation. SpaceX officially acquires xAI, merging rockets with AI expertise Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement While SpaceX is best known for its Falcon 9 and Starship rockets, the $1.75 trillion valuation is anchored by Starlink, its satellite internet service. Starlink ended 2025 with 9.2 million subscribers and over $10 billion in revenue, which is a figure analysts project could reach a staggering $24 billion by the end of 2026. A February all-stock merger with xAI, Musk’s artificial intelligence venture, further boosted the valuation. SpaceX officially acquires xAI, merging rockets with AI expertise Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement SpaceX officially acquires xAI, merging rockets with AI expertise Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement Bank of America, Goldman Sachs, JPMorgan Chase, and Morgan Stanley are lined up as senior underwriters. SpaceX is also considering a dual-class share structure to preserve insider voting control, and plans to allocate up to 30% of shares to retail investors, which is roughly three times the typical norm. Advertisement Elon Musk promises an official ceremony to mark the end of Tesla Model S and Model X production. Published on By Tesla has officially begun winding down production of the Model S and Model X, sending farewell emails to U.S. customers on March 27 and updating the website to reflect the end of the line. Shoppers visiting Tesla.com now find only a limited set of Model S and Model X inventory units available for purchase, with no option to configure a new factory build. The move formalizes what CEO Elon Musk announced on the company’s Q4 2025 earnings call in January, when he said it was “time to basically bring the Model S and X programs to an end with an honorable discharge.” Musk posted on X a throwback photo of himself speaking at the Model S production launch in 2012, and noting “We will have an official ceremony to mark the ending of an era. I love those cars.” The mention of an official ceremony is notable. Tesla has not held a formal farewell event for a vehicle before, and Musk’s wording suggests this will be something deliberate rather than a quiet line shutdown. Given that Musk’s X post shows a photo of him on stage with a microphone in front of an audience at the Fremont factory, it wouldn’t be too far-fetched to expect a closing ceremony to take place at the same location. Perhaps? Whether it becomes a public event, a private gathering for employees, or a livestreamed moment on X remains to be seen. Custom orders of the Tesla Model S & X have come to an end. All that’s left are some in inventory. We will have an official ceremony to mark the ending of an era. I love those cars.Advertisement This was me at production launch 14 years ago: pic.twitter.com/6kvCf9HTHc — Elon Musk (@elonmusk) April 1, 2026 The Model S first went on sale nearly fifteen years ago and was Tesla’s first fully in-house designed vehicle, proving that an electric car could be fast, desirable, and capable of long distance on a single charge. The Model X followed in 2015, turning heads with its unmistakable and distinctive falcon-wing doors, while becoming one of the first all-electric SUVs on the market. Tesla’s two flagship vehicles would ultimately push legacy automakers to take all-electric transportation seriously and help fund development of the more affordable Model 3 and Model Y.Advertisement By 2025, however, both models had been reduced to a rounding error in Tesla’s sales figures. Musk was direct about what comes next, stating “We are going to convert that production space to an Optimus factory. It’s part of our overall shift to an autonomous future.” Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement Musk posted on X a throwback photo of himself speaking at the Model S production launch in 2012, and noting “We will have an official ceremony to mark the ending of an era. I love those cars.” The mention of an official ceremony is notable. Tesla has not held a formal farewell event for a vehicle before, and Musk’s wording suggests this will be something deliberate rather than a quiet line shutdown. Given that Musk’s X post shows a photo of him on stage with a microphone in front of an audience at the Fremont factory, it wouldn’t be too far-fetched to expect a closing ceremony to take place at the same location. Perhaps? Whether it becomes a public event, a private gathering for employees, or a livestreamed moment on X remains to be seen. Custom orders of the Tesla Model S & X have come to an end. All that’s left are some in inventory. We will have an official ceremony to mark the ending of an era. I love those cars.Advertisement This was me at production launch 14 years ago: pic.twitter.com/6kvCf9HTHc — Elon Musk (@elonmusk) April 1, 2026 The Model S first went on sale nearly fifteen years ago and was Tesla’s first fully in-house designed vehicle, proving that an electric car could be fast, desirable, and capable of long distance on a single charge. The Model X followed in 2015, turning heads with its unmistakable and distinctive falcon-wing doors, while becoming one of the first all-electric SUVs on the market. Tesla’s two flagship vehicles would ultimately push legacy automakers to take all-electric transportation seriously and help fund development of the more affordable Model 3 and Model Y.Advertisement By 2025, however, both models had been reduced to a rounding error in Tesla’s sales figures. Musk was direct about what comes next, stating “We are going to convert that production space to an Optimus factory. It’s part of our overall shift to an autonomous future.” Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement The mention of an official ceremony is notable. Tesla has not held a formal farewell event for a vehicle before, and Musk’s wording suggests this will be something deliberate rather than a quiet line shutdown. Given that Musk’s X post shows a photo of him on stage with a microphone in front of an audience at the Fremont factory, it wouldn’t be too far-fetched to expect a closing ceremony to take place at the same location. Perhaps? Whether it becomes a public event, a private gathering for employees, or a livestreamed moment on X remains to be seen. Custom orders of the Tesla Model S & X have come to an end. All that’s left are some in inventory. We will have an official ceremony to mark the ending of an era. I love those cars.Advertisement This was me at production launch 14 years ago: pic.twitter.com/6kvCf9HTHc — Elon Musk (@elonmusk) April 1, 2026 The Model S first went on sale nearly fifteen years ago and was Tesla’s first fully in-house designed vehicle, proving that an electric car could be fast, desirable, and capable of long distance on a single charge. The Model X followed in 2015, turning heads with its unmistakable and distinctive falcon-wing doors, while becoming one of the first all-electric SUVs on the market. Tesla’s two flagship vehicles would ultimately push legacy automakers to take all-electric transportation seriously and help fund development of the more affordable Model 3 and Model Y.Advertisement By 2025, however, both models had been reduced to a rounding error in Tesla’s sales figures. Musk was direct about what comes next, stating “We are going to convert that production space to an Optimus factory. It’s part of our overall shift to an autonomous future.” Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement Custom orders of the Tesla Model S & X have come to an end. All that’s left are some in inventory. We will have an official ceremony to mark the ending of an era. I love those cars.Advertisement This was me at production launch 14 years ago: pic.twitter.com/6kvCf9HTHc — Elon Musk (@elonmusk) April 1, 2026 We will have an official ceremony to mark the ending of an era. I love those cars.Advertisement This was me at production launch 14 years ago: pic.twitter.com/6kvCf9HTHc — Elon Musk (@elonmusk) April 1, 2026 This was me at production launch 14 years ago: pic.twitter.com/6kvCf9HTHc — Elon Musk (@elonmusk) April 1, 2026 — Elon Musk (@elonmusk) April 1, 2026 The Model S first went on sale nearly fifteen years ago and was Tesla’s first fully in-house designed vehicle, proving that an electric car could be fast, desirable, and capable of long distance on a single charge. The Model X followed in 2015, turning heads with its unmistakable and distinctive falcon-wing doors, while becoming one of the first all-electric SUVs on the market. Tesla’s two flagship vehicles would ultimately push legacy automakers to take all-electric transportation seriously and help fund development of the more affordable Model 3 and Model Y.Advertisement By 2025, however, both models had been reduced to a rounding error in Tesla’s sales figures. Musk was direct about what comes next, stating “We are going to convert that production space to an Optimus factory. It’s part of our overall shift to an autonomous future.” Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement The Model S first went on sale nearly fifteen years ago and was Tesla’s first fully in-house designed vehicle, proving that an electric car could be fast, desirable, and capable of long distance on a single charge. The Model X followed in 2015, turning heads with its unmistakable and distinctive falcon-wing doors, while becoming one of the first all-electric SUVs on the market. Tesla’s two flagship vehicles would ultimately push legacy automakers to take all-electric transportation seriously and help fund development of the more affordable Model 3 and Model Y.Advertisement By 2025, however, both models had been reduced to a rounding error in Tesla’s sales figures. Musk was direct about what comes next, stating “We are going to convert that production space to an Optimus factory. It’s part of our overall shift to an autonomous future.” Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement By 2025, however, both models had been reduced to a rounding error in Tesla’s sales figures. Musk was direct about what comes next, stating “We are going to convert that production space to an Optimus factory. It’s part of our overall shift to an autonomous future.” Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement That shift is already underway. Tesla officially started Optimus Gen 3 production at its Fremont factory in January 2026, with the line targeting a run rate of one million units per year. The Gen 3 robot features 22 degrees of freedom per hand, runs on Tesla’s AI5 chip, and shares the same neural network architecture as Full Self-Driving. A dedicated Optimus factory at Gigafactory Texas is also under construction, with a planned annual capacity of 10 million units. The production lines that once built the Model S and Model X are being converted to support that ramp. Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement Tesla confirmed it will continue to support existing owners with service, software updates, and parts for as long as people own the vehicles. For buyers still interested in a new example, remaining U.S. inventory is discounted and the window is closing fast.Advertisement NASA’s Artemis II launches Wednesday, sending humans near the Moon for the first time since 1972. Published on By For the first time since Apollo 17 touched down on the lunar surface in December 1972, the United States is sending humans back toward the Moon. NASA’s Artemis II mission is set to launch as early as this week from Kennedy Space Center in Florida, carrying four astronauts on a 10-day journey around the Moon and back to Earth. It will not land anyone on the surface this time, but it is the first crewed flight in over half a century to travel beyond low Earth orbit, and it sets the stage for Elon Musk’s SpaceX missions to follow. The mission uses NASA’s Space Launch System rocket and the Orion spacecraft, which will fly around the Moon before splashing down in the Pacific Ocean around April 10. For context, an uncrewed Artemis I flew the same path in 2022, proving the hardware worked. Artemis II now tests it with people aboard. According to NASA’s official countdown blog, launch preparations are on track with an 80 percent chance of favorable weather. “Hey, let’s go to the moon!” Commander Wiseman told reporters upon arriving at Kennedy Space Center. Source: NASA Beyond Artemis II lies the lander question, and that is where SpaceX enters directly. In 2021, NASA awarded SpaceX a $2.89 billion contract to develop the Starship Human Landing System, a modified version of Starship designed to ferry astronauts from lunar orbit to the surface. The original plan called for SpaceX to deliver that lander for Artemis III, which was to be the first crewed lunar landing. Timing for Starship development, however, caused NASA to restructure the mission sequence entirely. Before SpaceX’s Starship Human Landing System (HLS) can put anyone on the Moon, it has to solve a problem no rocket has demonstrated at scale, which is refueling in orbit. Because the Starship HLS requires approximately ten tanker launches worth of propellant loaded into a depot in low Earth orbit before it has enough fuel to reach the lunar surface, SpaceX plans to conduct this refueling process using its upgraded V3 Starship. And until that demonstration flies and succeeds, the Starship moon lander remains a question mark.Advertisement SpaceX’s Starship V3 is almost ready and it will change space travel forever In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement The mission uses NASA’s Space Launch System rocket and the Orion spacecraft, which will fly around the Moon before splashing down in the Pacific Ocean around April 10. For context, an uncrewed Artemis I flew the same path in 2022, proving the hardware worked. Artemis II now tests it with people aboard. According to NASA’s official countdown blog, launch preparations are on track with an 80 percent chance of favorable weather. “Hey, let’s go to the moon!” Commander Wiseman told reporters upon arriving at Kennedy Space Center. Source: NASA Beyond Artemis II lies the lander question, and that is where SpaceX enters directly. In 2021, NASA awarded SpaceX a $2.89 billion contract to develop the Starship Human Landing System, a modified version of Starship designed to ferry astronauts from lunar orbit to the surface. The original plan called for SpaceX to deliver that lander for Artemis III, which was to be the first crewed lunar landing. Timing for Starship development, however, caused NASA to restructure the mission sequence entirely. Before SpaceX’s Starship Human Landing System (HLS) can put anyone on the Moon, it has to solve a problem no rocket has demonstrated at scale, which is refueling in orbit. Because the Starship HLS requires approximately ten tanker launches worth of propellant loaded into a depot in low Earth orbit before it has enough fuel to reach the lunar surface, SpaceX plans to conduct this refueling process using its upgraded V3 Starship. And until that demonstration flies and succeeds, the Starship moon lander remains a question mark.Advertisement SpaceX’s Starship V3 is almost ready and it will change space travel forever In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement According to NASA’s official countdown blog, launch preparations are on track with an 80 percent chance of favorable weather. “Hey, let’s go to the moon!” Commander Wiseman told reporters upon arriving at Kennedy Space Center. Source: NASA Beyond Artemis II lies the lander question, and that is where SpaceX enters directly. In 2021, NASA awarded SpaceX a $2.89 billion contract to develop the Starship Human Landing System, a modified version of Starship designed to ferry astronauts from lunar orbit to the surface. The original plan called for SpaceX to deliver that lander for Artemis III, which was to be the first crewed lunar landing. Timing for Starship development, however, caused NASA to restructure the mission sequence entirely. Before SpaceX’s Starship Human Landing System (HLS) can put anyone on the Moon, it has to solve a problem no rocket has demonstrated at scale, which is refueling in orbit. Because the Starship HLS requires approximately ten tanker launches worth of propellant loaded into a depot in low Earth orbit before it has enough fuel to reach the lunar surface, SpaceX plans to conduct this refueling process using its upgraded V3 Starship. And until that demonstration flies and succeeds, the Starship moon lander remains a question mark.Advertisement SpaceX’s Starship V3 is almost ready and it will change space travel forever In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement Source: NASA Beyond Artemis II lies the lander question, and that is where SpaceX enters directly. In 2021, NASA awarded SpaceX a $2.89 billion contract to develop the Starship Human Landing System, a modified version of Starship designed to ferry astronauts from lunar orbit to the surface. The original plan called for SpaceX to deliver that lander for Artemis III, which was to be the first crewed lunar landing. Timing for Starship development, however, caused NASA to restructure the mission sequence entirely. Before SpaceX’s Starship Human Landing System (HLS) can put anyone on the Moon, it has to solve a problem no rocket has demonstrated at scale, which is refueling in orbit. Because the Starship HLS requires approximately ten tanker launches worth of propellant loaded into a depot in low Earth orbit before it has enough fuel to reach the lunar surface, SpaceX plans to conduct this refueling process using its upgraded V3 Starship. And until that demonstration flies and succeeds, the Starship moon lander remains a question mark.Advertisement SpaceX’s Starship V3 is almost ready and it will change space travel forever In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement Before SpaceX’s Starship Human Landing System (HLS) can put anyone on the Moon, it has to solve a problem no rocket has demonstrated at scale, which is refueling in orbit. Because the Starship HLS requires approximately ten tanker launches worth of propellant loaded into a depot in low Earth orbit before it has enough fuel to reach the lunar surface, SpaceX plans to conduct this refueling process using its upgraded V3 Starship. And until that demonstration flies and succeeds, the Starship moon lander remains a question mark.Advertisement SpaceX’s Starship V3 is almost ready and it will change space travel forever In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement SpaceX’s Starship V3 is almost ready and it will change space travel forever In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement In February 2026, NASA Administrator Jared Isaacman confirmed that Artemis III, now planned for mid-2027, and will instead test lunar landers in low Earth orbit, with the actual landing pushed to Artemis IV that’s targeted for 2028. Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement Musk responded to earlier criticism of SpaceX’s schedule by posting on X that his company is “moving like lightning compared to the rest of the space industry,” and added that “Starship will end up doing the whole Moon mission.” The contract competition was also reopened in October 2025 by then NASA chief Sean Duffy, who cited Starship’s delays and said the agency needed speed given China’s own stated goal of landing astronauts on the Moon by 2030. They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement They won’t. SpaceX is moving like lightning compared to the rest of the space industry. Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 Moreover, Starship will end up doing the whole Moon mission. Mark my words.Advertisement — Elon Musk (@elonmusk) October 20, 2025 — Elon Musk (@elonmusk) October 20, 2025 Artemis came from the first Trump administration’s 2017 Space Policy Directive 1, which directed NASA to return humans to the Moon. The program picked up pace through the 2020s, with the Orion spacecraft and SLS taking years to develop at enormous costs. SpaceX entered the picture in 2021 as the chosen lander contractor, tying the commercial space sector into what had historically been an all government undertaking. Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement Whether SpaceX’s Starship ultimately carries astronauts to the lunar surface or shares that role with Blue Origin’s competing lander, this week’s Artemis II launch is the necessary first step. Getting four humans to the Moon’s vicinity and back safely is the proof of concept everything else depends on. Advertisement How to give your Tesla a Custom Lovk Sound! Easy tutorial!! #tesla #teslatok #teslalocksound Copyright © TESLARATI. All rights reserved.

Sign Up! envelope_alt Get the latest Android News in your inbox every day arrow_right Sign up to receive the latest Android News every weekday: Only send updates once a week Android Headlines / Mobile Events News / CES / CES 2026 Showcases Emotionally Intelligent Robots for All Age Groups Mind With Heart Robotics Co., Ltd. has showcased its new emotionally intelligent robots at the CES 2026. The robots are designed to complement and support both the elders and the children. It uses artificial intelligence and clinically backed algorithms to adapt naturally to the user. The Consumer Electronics Show (CES) 2026 is going on full swing, and now, Mind With Heart Robotics Co., Ltd. has unveiled a broad portfolio of emotionally intelligent robots at the show. These robots are designed to meet the emotional and therapeutic needs of individuals across various age groups. The company showcases their robots’ natural movement, tactile interaction, and affective intelligence that adapts over time. The tech giant says that its emotionally intelligent robots are designed for both older people’s companionship and pediatric therapy. It shows how social robotics is moving toward clinically informed, human-centered design at a global scale for future care ecosystems worldwide. Robots are no longer limited to just mechanical and work-related tasks. The lineup is a result of years of research in affective computing and human-robot interaction led by founder and CEO Zhang Jiaming. The CEO has more than a decade of experience in the field. He has also overseen dozens of robotic systems and filed extensive patents in biomimetic design and emotional AI. With such extensive experience and knowledge, he designed the robots that read touch, voice, and behavior patterns and can also respond with lifelike motion. Further, it also keeps clinical collaboration and data ethics in mind for long-term safety and accuracy. The best part about the robots is that they can adapt to sensitive care settings in homes, hospitals, and schools across different global markets today. The main highlight of the show was the new An’An panda cub robot. It was also honored by the Consumer Technology Association with the CES Innovation Awards in artificial intelligence. It is designed specifically for loneliness and the care of old-aged people. The robot uses full-body tactile sensing and long-term memory to personalize interaction. Alongside An’An, the firm showcased its Duncan Series companion robots. These are meant for pediatric therapy, including support for children with autism and sensory challenges. The lineup is made while keeping skill-spanning communication, social interaction, motor development, play, and emotional well-being in mind. Mind With Heart Robotics says that they’re planning for a commercial release of all the robots in March. The products would be accessible at a worldwide scale across consumer, healthcare, and institutional markets. I am an experienced consumer tech writer dedicated to producing comprehensive guides and news that empower readers. My passion for technology drives me, and you can often find me exploring Tech Twitter. Feel free to reach out to me at: [email protected]. Copyright ©2026 Android Headlines. All Rights Reserved. Main Deals & More Android News Sign Up! envelope_alt Get the latest Android News in your inbox every day arrow_right Sign up to receive the latest Android News every weekday: Only send updates once a week

Switch between CA and NY editions here. A McDonald’s in a Chinese city welcome humanoid robots to serve up meals and entertain customers — but only for a limited time. Videos posted on social media captured the myriad of lifelike robots at a McDonald’s in Shanghai performing routine tasks typically completed by human workers, such as greeting customers and delivering food. Diners were seen interacting with the robots dressed in the fast-food joint’s iconic red-and-yellow uniforms behind counters, while children chased more of the moving machinery disguised as cute animals. The robots, supplied by Chinese firm Keenon Robotics, were deployed as part of a trial at the McDonald’s location, Digitaltrends reported. McDonald’s said the robots were only around for five days — from March 14 to the 19 — and were meant to plug the grand opening of the Shanghai Science and Technology Museum restaurant. “Our Humanoid series are leading the squad and hitting the streets,” Keenon Robotics posted on social media alongside a clip of the robots interacting with diners. “It’s a showcase of how service automation is becoming a seamless part of global dining, and how technology brings more smiles to every mealtime,” the company added. Jon Banner, the executive vice president and global chief impact officer of the beloved fast-food giant, tweeted that the robots were there for a “temporary greeting.” “Mission accomplished!” he said. “The robots were not involved in any service or operational functions. And if you didn’t visit prior to today, you missed them.” The footage comes amid concerns over artificial intelligence and robots replacing tasks typically completed by human workers at large corporations. In July, the Wall Street Journal reported that Amazon will soon use more robots in its warehouses than human employees, with more than 1 million machines already deployed across facilities. Many of these robots handle the heavy lifting in warehouse work, picking items from tall shelves and moving goods around facilities. Others are advanced enough to help humans sort and package orders, according to the Wall Street Journal. Three-quarters of Amazon’s global deliveries are now assisted by robots in some way, according to the company.

Nel settore della robotica domestica, i video dimostrativi sono ormai una sorta di tradizione. Tra le aziende più attive in tal senso c’è Figure AI. Quest’ultima, infatti, ha attirato molta attenzione grazie ai suoi robot umanoidi progettati per lavorare con gli esseri umani. L’ultimo protagonista di tali dimostrazioni è Figure 03. Si tratta di un modello pensato per affrontare attività domestiche. Il nuovo video pubblicato dall’azienda mostra il robot impegnato in una piccola routine casalinga. Si muove tra mobili e oggetti raccogliendo giocattoli lasciati sul pavimento, sistema i cuscini del divano e passa un panno su alcune superfici per pulirle. Scene simili potrebbero sembrare quasi banali, ma proprio la loro normalità è ciò che rende interessante la dimostrazione. L’obiettivo non è stupire con movimenti spettacolari, ma dimostrare che un robot può interagire con un ambiente domestico reale, dove nulla è perfettamente ordinato. Non è la prima volta che l’azienda mostra le capacità dei suoi robot. Già in passato il precedente modello, Figure 02, aveva dato prova di una notevole abilità nella manipolazione degli oggetti. In alcune dimostrazioni lo si vedeva selezionare capi di abbigliamento o organizzare oggetti con movimenti precisi. Con il nuovo robot l’attenzione sembra spostarsi ancora di più sulla gestione di situazioni domestiche meno prevedibili. Alla base di tali capacità c’è il sistema AI sviluppato dall’azienda, chiamato Helix 02. Tale piattaforma integra diversi elementi fondamentali per la robotica moderna. Tra cui la visione artificiale per riconoscere oggetti e ambienti, la comprensione del linguaggio per interpretare istruzioni. A ciò si aggiunge anche una componente di pianificazione che traduce le informazioni raccolte in azioni concrete. Un dettaglio interessante riguarda la velocità del robot. Osservando il video, si nota che i movimenti sono ancora più lenti rispetto a quelli di una persona. Non si tratta di un limite tecnologico, ma una scelta legata alla sicurezza. In un ambiente domestico, dove il robot potrebbe trovarsi vicino a persone o animali, mantenere movimenti controllati e prevedibili riduce i rischi. Nonostante i progressi mostrati nel video, Figure AI non ha ancora annunciato quando robot come Figure 03 potrebbero arrivare sul mercato. Prima di una commercializzazione sarà necessario raccogliere grandi quantità di dati e dimostrare che il sistema può funzionare in modo affidabile. Ciao sono Margareth, per gli amici Maggie, la vostra amichevole web writer di quartiere. Questa piccola citazione dice già tanto di me: amo il cinema, le serie tv, leggere e cantare a squarciagola i musical a teatro. Se a questo aggiungiamo la passione per la fotografia e la tecnologia direi che è facile intuire perché ho deciso di studiare e poi lavorare con la comunicazione. 2012 – 2026 Tecnoandroid.it – Gestito dalla STARGATE SRLS – P.Iva: 15525681001 Testata telematica quotidiana registrata al Tribunale di Roma CON DECRETO N° 225/2015, editore STARGATE SRLS. Tutti i marchi riportati appartengono ai legittimi proprietari. Questo articolo potrebbe includere collegamenti affiliati: eventuali acquisti o ordini realizzati attraverso questi link contribuiranno a fornire una commissione al nostro sito. 🔥 Non perderti nemmeno un'offerta Smartphone, notebook, gadget tech al prezzo più basso. Unisciti a migliaia di lettori di TecnoAndroid! Puoi disiscriverti in qualsiasi momento. Niente spam, solo offerte vere. 🎯 Inserisci qualcosa di speciale: Tienimi connesso fino a quando non esco Password dimenticata? Ti sarà inviata una nuova password via email. Hai ricevuto una nuova password? Accedi qui.

In this article The White House hosted its "first humanoid robot guest" on Wednesday, with first lady Melania Trump appearing alongside a robot from robotics upstart Figure AI. The robot, identified as Figure 3, accompanied the first lady during the second day of the Fostering the Future Together Global Coalition Summit, a gathering focused on technology and children's education. The machine greeted attendees in multiple languages and described itself as "a humanoid built in the United States of America," according to widely circulated footage from the event. The display represented one of, if not the, highest-profile showcases of humanoid robotics in the U.S. to date and highlights how the tech is becoming a national priority amid global tech competition. Beijing has also promoted humanoid robots at highly publicized events this year. The first lady used the robot to promote her push for artificial intelligence in children's education, suggesting that the robots could one day act as interactive educators at home. However, Figure AI says its third-generation humanoids are also applicable for more general purposes, including commercial and household tasks. The White House spotlight is likely to boost the brand of Nvidia-backed Figure AI, a lesser-known robot company compared to larger humanoid players like Tesla's Optimus and Boston Dynamics, though some of its team comes from those competitors, as well as tech giants like Apple. Figure AI was founded in 2022 by Brett Adcock, a tech entrepreneur and billionaire who previously co-founded the publicly traded aerospace company Archer Aviation and a digital hiring marketplace Vettery. Powering its robots is the firm's in-house Helix AI system, a vision-language-action model that powers its robots and enables learning through observation and verbal commands. Amid growing investor excitement for physical AI, the firm raised more than $1 billion in its Series C funding round in September led by Parkway Venture Capital with participation from other notable investors such as Nvidia, Intel Capital, Qualcomm Ventures and Salesforce. That gave it a post-money valuation of $39 billion. The fundraising is expected to be put towards the firm's aim to deploy thousands of robots in homes and logistics over the coming years — a goal that has likely been made easier by a major endorsement from the White House. Figure AI has already begun work with its first commercial customer in BMW, deploying its robots for tasks like handling sheet metal parts in manufacturing facilities. It's possible that Melania's endorsement of Figure AI's robots as potential educators will trigger a reexamination of an ongoing lawsuit the company found itself in last year. In November, Figure AI was sued by its former head of product safety, who alleged he was fired after warning executives that the company's robots were powerful enough to fracture a human skull. Robert Gruendel filed the complaint in federal court in California, claiming wrongful termination after raising safety concerns with CEO Brett Adcock and chief engineer Kyle Edelberg in September 2025. The suit stated that Figure AI's next-generation robots moved at superhuman speed and generated force approximately twice the level necessary to fracture an adult human skull. Gruendel also alleged that one robot had carved a gash into a steel refrigerator door during a malfunction. Figure AI contends that Gruendel had been fired for poor performance, and described the allegations as "falsehoods." Figure AI countersued in January, saying Gruendel failed in his role to help the company build a safe robot. The lawsuit drew attention to broader questions about safety standards in humanoid robotics development and remains pending. Interestingly, the White House event on Wednesday wasn't the first time that a company connected to Adcock received some major shine from the Trump administration. Shares of the aerospace company he co-founded, Archer Aviation, surged in June last year after U.S. President Donald Trump signed an Executive Order directing the establishment of a program to promote the safe integration of electric air taxis in U.S. cities. Archer is participating in the initiative and is working on projects involving aircraft demonstrations. Following the June 2025 executive order, Archer raised $850 million in a registered direct stock offering. Adcock co-founded Archer Aviation in 2018 with Adam Goldstein and initially served as co-CEO. However, Adcock stepped down in April 2022, and then resigned from the company's board of directors shortly afterward. He remains a shareholder, according to investment research platform Business Quant, but he has no active executive, board, or advisory position at the company. Correction: This story has been updated to reflect that Archer Aviation is an aerospace company. An earlier version of the story gave an incorrect description of the firm's business. Got a confidential news tip? We want to hear from you. Sign up for free newsletters and get more CNBC delivered to your inbox Get this delivered to your inbox, and more info about our products and services. © 2026 Versant Media, LLC. All Rights Reserved. A Versant Media Company. Data is a real-time snapshot *Data is delayed at least 15 minutes. Global Business and Financial News, Stock Quotes, and Market Data and Analysis. Data also provided by

Advertisement Humanoid robots are machines designed to resemble the human body and replicate some humanlike abilities. Engineers in humanoid robotics build machines with arms, legs, and sensors that allow them to perform tasks in environments built for human beings. Unlike many traditional industrial robots used in factories, humanoid robots aim to work alongside humans in real world settings. Their humanlike structure helps them open doors, use tools, and interact with human operators. Advertisement Rapid advances in artificial intelligence, machine learning, and robot hardware are pushing these systems from science fiction into reality. Researchers now test advanced humanoid robot platforms in homes, workplaces and public spaces. Most humanoid robots copy the basic body plan of their human counterparts. Engineers design them with a torso, head, robotic arms, and bipedal robots legs that allow humanlike movements. Complex mechanical components and motors give these machines many degrees of freedom, meaning they can move joints in multiple directions. This flexibility helps robots perform complex tasks that require human dexterity. Advertisement Sensors such as cameras, tactile sensing systems and force/torque sensors allow a robot to detect objects, adjust its grip, and maintain balance in complex environments. Modern humanoid robot designed systems rely heavily on artificial intelligence. AI models help robots understand surroundings, track objects, and plan actions. Developers train AI models using machine learning techniques such as imitation learning and reinforcement learning. These methods allow robots to learn new skills by observing humans or experimenting with actions. Advertisement Data pipelines and control systems process information from sensors so the robot can react in real time. This tracking ability helps humanoid robots navigate unstructured environments and maintain safe human robot interaction. Several companies and research groups are developing humanoid robotics platforms. Boston Dynamics has explored agile robots capable of moving through difficult terrain. Agility Robotics created Digit robots designed for tasks such as carrying packages and moving totes in warehouses. Pal Robotics builds humanoid service robot systems used as development platforms for research. Advertisement Other humanoid robots come from companies such as SoftBank Robotics, Hanson Robotics, and Engineered Arts. These machines often focus on social robot roles, customer service roles, or public demonstrations that showcase facial expressions and communication abilities. Humanoid robots can perform some manual tasks that once required human workers. Robotic arms and motor control allow some humanoid robots to manipulate tools or handle objects. Developers are training robots to help with household tasks such as cleaning or organizing items. In industrial settings, autonomous robots may assist humans with assembling parts, transporting materials, or monitoring equipment. Advertisement Some robots can also be controlled remotely using remote control systems. Human operators guide the machine while the robot provides mobility and strength in dangerous or distant environments. Many experts believe the first wave of humanoid robots will appear in workplaces where labor shortages exist. These robots may help complete repetitive or physically demanding tasks while working alongside humans. Researchers continue improving balance, autonomous navigation, and humanlike motion so robots can operate in various environments. Advances in greater dexterity and machine perception may allow robots to interact more naturally with people. Advertisement While fully autonomous humanoid machines remain in early stages, ongoing research described in publications such as IEEE Spectrum shows steady progress. As artificial intelligence improves, humanoid robots may become capable assistants in homes, hospitals and workplaces across the world. We created this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor. Advertisement Please copy/paste the following text to properly cite this HowStuffWorks.com article: Advertisement Advertisement Advertisement Advertisement Advertisement

Brett Adcock has built and sold companies in robotics, security and air taxis, and now he wants to reinvent how people use A.I. His latest venture, Hark, is a new lab that pairs personalized intelligence with custom-built hardware. Instead of specializing in models or devices alone, Hark aims to own the whole pipeline—foundation models, software systems, hardware and user interfaces—under one roof. The company has recruited top talent from Apple and Meta to build an A.I. product that better bridges the gap between humans and machines. Thank you for signing up! By clicking submit, you agree to our <a href="http://observermedia.com/terms">terms of service</a> and acknowledge we may use your information to send you emails, product samples, and promotions on this website and other properties. You can opt out anytime. “The A.I. systems I use today are far from my vision of what the future should be,” said Adcock in a statement. “We want to create intelligence that lets you offload your mental workload into a system that begins to think like you and sometimes ahead of you.” Hark is the latest in a string of ambitious projects launched by Adcock. He previously funded the hiring marketplace Vettery; Archer, which builds electric vertical takeoff and landing aircraft (eVTOLs); and Cover, an A.I. security company developing weapon-detection systems. Hadcock also remains CEO of Figure, a robotics startup he founded in 2022 that is developing humanoid robots to automate labor. Figure, which is testing A.I. agents on its robots but will remain a separate company from Hark, was most recently valued at $39 billion in 2025. For now, Hark is financed entirely by Adcock’s own money: $100 million in personal capital. The entrepreneur, who has an estimated net worth of $19.1 billion, wants to build multimodal A.I. systems that handle speech, text, vision and context, layered with personalized memory, proactive behavior and real-time speech capabilities. Those systems are meant to work hand in hand with Hark’s own hardware. Leading that effort is Abidur Chowdhury, hired as head of design after seven years as an industrial designer at Apple, where he worked on iPhone and Mac products such as the recent iPhone Air. “We believe that the future is a new interface that will understand you, intelligently anticipate your needs, and love doing tasks that you don’t want to do,” said Chowdhury in a statement. Hark’s broader team includes A.I. researchers and engineers drawn from some of Silicon Valley’s biggest firms. On the hardware side, hires include longtime Apple staffers like David Narajowski and Dave Wilkes, who worked on product development architecture and audio hardware systems. On the A.I. side, the company has brought in senior researchers from Meta’s Superintelligence Lab, including Mingbo Ma, Xubo Liu, Xianfeng Rui, Kainan Peng and Zhihong Lei. Hark’s headcount, which also includes talent from Google, Amazon and Tesla, is about 45 today and is expected to reach 100 in the first half of 2026. To speed up model development, Hark has struck a compute deal with Nvidia that will bring thousands of GPUs online next month for pre-training and post-training its systems. Hark is entering a crowded field of ventures trying to rethink how people interact with A.I. OpenAI has enlisted former Apple design chief Jony Ive for a still-secret device project, while Meta is betting heavily on A.I.-enabled smart glasses. Newer hardware startups like Sandbar have raised millions to develop wearables with personalized A.I. at their core. Adcock says Hark will begin releasing its first A.I. models this summer, followed shortly by hardware devices designed around those systems. “We believe the next computing platform will be personal A.I.—intelligence that understands you and works alongside you every day,” he said. “But that future only becomes possible when the entire stack is built together.” We get it: you like to have control of your own internet experience. But advertising revenue helps support our journalism. To read our full stories, please turn off your ad blocker.We'd really appreciate it. Below are steps you can take in order to whitelist Observer.com on your browser: Click the AdBlock button on your browser and select Don't run on pages on this domain. Click the AdBlock Plus button on your browser and select Enabled on this site. Click the AdBlock Plus button on your browser and select Disable on Observer.com.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Communications volume 17, Article number: 2455 (2026) Cite this article 6026 Accesses 7 Altmetric Metrics details Softening and onboarding computers and controllers is one of the final frontiers in soft robotics towards their robustness and intelligence for everyday use. In this regard, embodying soft and physical computing presents exciting potential. Physical computing seeks to encode inputs into a mechanical computing kernel and leverage the internal interactions among this kernel’s constituent elements to compute the output. Moreover, such input-to-output evolution can be re-programmable. This perspective paper proposes a framework for embodying physical computing into soft robots and discusses three unique strategies in the literature: analog oscillators, physical reservoir computing, and physical algorithmic computing. These embodied computers enable the soft robot to perform complex behaviors that would otherwise require CMOS-based electronics — including coordinated locomotion with obstacle avoidance, payload weight and orientation classification, and programmable operation based on logical rules. This paper will detail the working principles of these embodied physical computing methods, survey the current state-of-the-art, and present a perspective for future development. The dream of creating entirely soft, versatile, and capable robots—akin to the octopus—has long inspired scientists and engineers. We have witnessed significant progress in soft actuation1,2, sensing3, and power4, enabling these robots to operate in a wide range of challenging environments, from deep within our own bodies5 to the far bottom of the Mariana trench6. Yet, softening and onboarding computers and controllers remain a major challenge and present one of the final frontiers towards robust and intelligent soft robots suitable for everyday use. In this regard, roboticists have long recognized that the inherent material softness can facilitate and simplify control, and many innovative strategies have been explored. For example, soft and rotating legs can naturally accommodate uneven surfaces and large obstacles, allowing the robot to traverse challenging terrains without complex controls like in the quadrupeds7. Soft curling tentacles can wrap and entangle themselves around objects with widely different shapes, thus manipulating them with a simple global pressure input8. Such softness-facilitated control is sometimes referred to as “intelligence by mechanics”9 or “morphological computation”10,11. They offer exciting potential, but frequently lack the sophistication and (re-) programmability available from the more universal controllers based on digital computation. In parallel with the advancements in soft robotics (and partly inspired by the need for soft robotic computing and control), there is also growing interest in CMOS-free physical computers12,13,14,15,16. This emerging paradigm seeks to encode physical inputs into a mechanical construct (or kernel)—for example, in the form of deformation17,18,19,20,21, fluid flow22,23, thermal heat flux24, or waves25,26,27,28,29—and leverage the internal interactions among the kernel’s constituent elements to process these inputs according to a programmed evolution. The resulting output typically remains in the same physical domain as the input so that it can be easily decoded and interpreted. The paths to physical computing are diverse: one can use acoustic waves to solve differential equations28,30, re-purpose mechanical vibrations like neuron signals to perform machine learning tasks10,31,32, or construct mechanical logic gates and physical circuitry to perform algorithmic operations33. Overall, the idea of performing computation without CMOS electronics could benefit us with higher energy efficiency34, parallelization25, and resiliency against adversarial working conditions. Therefore, there is a tremendous opportunity to introduce physical computing into the field of soft robotics. That is, one could construct a physical computer out of soft materials and integrate it with soft sensors and actuators. Such integration can lead to a new class of entirely soft computation and control methods with flexibility, robustness, and programmability for more sophisticated tasks. As a result, we have witnessed a rapid emergence of soft robots with integrated and embodied physical computers over the past several years. And these physically computing robots have become an important part of recent reviews that offer a birds-eye overview on embodied intelligence (or mechanical intelligence, physical control) in robotics35,36,37,38. On the other hand, we believe a separate, deeper dive into physical computing in soft robots can benefit the research community. Specifically, we aim to define physical computing using a rigorous framework, including encoding, decoding, and a (re-)programmable computing kernel, and build upon this definition to categorize physical computing into two distinct types: analog and algorithmic (more in the following Section “what is physical computing (and what is not)?”). In this way, we can dissect and re-examine soft robotics through a different lens. We will also have a more systematic framework to introduce new physical computing concepts from other disciplines to robotics. Therefore, this perspective paper will first establish a more formal framework for physical computing, then survey the different analog and algorithmic computers implemented in the soft robots, and discuss challenges and future directions in the end. Before surveying soft robots with embodied computing, we should first clarify the definition of a physical computer. In the robotics literature, the scope of “computing” and “intelligence” has been quite broad and occasionally conflicting. We certainly do not intend to propose a new definition that everyone would agree upon. Instead, we would like to highlight a few key ingredients of the physical computer to anchor the scope of this particular paper. To this end, we propose that physical computing should involve two domains: one is the agents using the computer. They could be a human operator, but in this study, they typically refer to the non-computing parts of a soft robot, including sensors, actuators, and power supply. The agents will have an “input” that they wish to use, and expect an “output” from the computing. The second domain is the kernel, where the physical interaction between its constituent components embodies the computing program. Under this formalism, a complete physical computer should (1) have a mechanism to encode inputs from the agents into the computing kernel and decode the outputs correspondingly, and (2) have a mechanism to program (i.e., design and configure) the evolution from the input to the output in the computing kernel (Fig. 1a). a The computing architecture adapted in this perspective includes input encoding, output decoding, and programmable input-output evolution. b Analog computing: the harmonic analyzer is an elegant example of analog computing from the 1800s (photo credit to the Nimitz Library, United States Naval Academy). In the modern electronic computing paradigm, artificial neural networks also operate on analog (continuous) data. We will discuss two types of physical analog computing in soft robots: oscillator and reservoir. For example, an electronics-free legged robot uses an analog oscillator to walk70 (photo credit to David Baillot, Jacobs School of Engineering, UC San Diego), and a modular manipulator uses embodied reservoir computing to classify payloads (image adapted from ref. 145 CC BY 4.0). c Algorithmic computing: the difference machine is one of the first algorithmic computers (photo credits to Science Museum London, CC Attribution-SA 2.0). Modern CMOS-based computers are built exclusively on algorithmic Boolean logic. Here, we survey how mechanical logic is implemented in soft robots. For example, a robotic hand operates with fluidic logic control (image adapted from ref. 112 CC BY 4.0). Therefore, in this paper, computing does not exist without encoding, decoding, and programming39. Under this formalism, some nonconventional and innovative computing paradigms in the robotics field, such as the aforementioned “morphological computation,” are beyond our scope. Morphological computation generally refers to the idea that a robot body’s shape, deformation, and dynamics can perform part of the “computation” needed for control. Under this paradigm, “computation” can be quite diverse—it can be storing and releasing energy periodically to stabilize locomotion (e.g., passive walker40), or conforming to complex objects to assist manipulation (e.g., vacuum jamming gripper41), or pre-processing sensory data to assist perception (e.g., bat ear that mechanically process the incoming sound waves to assist object localization42). Therefore, the physical computing defined in this paper can be an example of morphological computation, but it has a more structured definition. That is, many morphological computation examples will not be considered as physically computing in this paper because they do not have the “encoding-kernel evolution-decoding” architecture, and they are not reprogrammable. On the other hand, a mechanical construct—e.g., architected materials or soft robotic body—that can incorporate encoding, decoding, and programming would meet the necessary condition to function as a physical computer. Moreover, based on these definitions, we will adopt the theory from Jaeger et al., and categorize physical computers into two sets (Fig. 1b)43,44. One is analog, where input and output signals are continuous, and the evolution from input to output is governed by smooth (and frequently physics-based) functions. Albert Michelson’s harmonic analyzer45 and our human neural system are classical examples of analog computers. In soft robots, this can be accomplished by exploiting their bodies’ nonlinear dynamic responses for physical reservoir computing (PRC). The other type of physical computer is the algorithmic, where the input and output take a discrete format, and the evolution from input to output is programmed via a set of abstract logical rules. Charles Babbage’s difference machine46 and our omnipresent CMOS-based computing chips are classical examples of an algorithmic computer. In soft robots, this can be accomplished by, for example, an assembly of mechanical Boolean gates featuring elastic bistability (i.e., mechanical logic gates). Table 1 summaries and compares the different computing approaches from this perspective. It is worth highlighting that many soft robots use responsive materials to interact with their surrounding environment and achieve adaptive behaviors. However, they do not necessarily compute according to the above-mentioned definition. Materials are considered “responsive” or “active” if they can change their shape or constitutive properties in response to external stimuli, such as temperature47, heat flux48, electric field49, magnetic field47,50, light48,51, and humidity52. They were initially introduced to soft robotics as artificial muscles. Shape memory alloys (SMAs) have been widely utilized in soft robotics since their inception53. Dielectric elastomer is another example54, and some liquid format dielectric materials can generate very high output forces to create jellyfish-like soft robots49. A programmable electrothermal actuator using silver nanowires (AgNW) can enable a robot to crawl55. One can also harvest responsive materials from nature, such as the self-drilling seed carrier made from white oak tissue, which can autonomously burrow by exploiting ambient humidity cycles52. (Interested readers can refer to the excellent reviews in refs. 56,57 for a comprehensive survey of responsive materials used for robotic actuation.) As responsive materials continue to evolve, researchers are beginning to explore how they can be strategically embedded in soft robotic bodies to facilitate and simplify control. For example, untethered robots with responsive materials can achieve simple and remote operation58, thereby reducing the associated control and computational complexity. Examples like this include miniature magnetic shape-programmable robots47,50, m-PDMS (magnetic particle-doped polydimethylsiloxane) robots59,60,61, photoresponsive LCE robots51, and piezoelectric polyvinylidene fluoride (PVDF) robots62. By integrating different types of responsive materials in one body, simple computational capabilities48,63 can be achieved (e.g., a soft robot that turns toward light only if heat is also present). However, although these responsive materials can enable complex tasks without sophisticated controllers (which suggests some intelligence in the mechanical domain, or mechano-intelligence64,65), they are not considered computing in this study due to a lack of clear mechanisms for input encoding, output decoding, and programmable input-output evolution. Instead, responsive materials can serve as the building blocks of physical computing, and we hope this will become clear as we survey soft robots with physical computing in the following sections. Rhythmic motions here refer to periodic changes in the shape or configuration of a soft structure over time. They are omnipresent in the animal kingdom, such as breathing, heart beating, and in particular, locomotions like walking, swimming, and wing flapping66. The underpinning mechanisms to generate rhythmic motions are diverse and still active topics of research. Among them, the central pattern generator (CPG) is a unique mechanism that can be considered as a physical computer and thus directly relates to this study. CPG is a self-organized neural circuit that produces rhythmic output from a simple, nonrhythmic input, and the input-output evolution is programmed by the neural circuit’s architecture. The CPG makes it possible to achieve and reconfigure complex locomotion gaits with minimal involvement from the brain or local sensory feedback67,68. The striking simplicity and capability of the CPG have inspired similar implementations in soft and continuous robots, where an analog oscillator—either electric or mechanical—is integrated to generate rhythmic deformation from a simple (and typically constant) input to drive locomotion69. Although many of these oscillators are not as complex as CPG’s neural circuit, their underlying computing principle are similar. An example of analog oscillator applied to soft robots: in the quadruped robot shown in Fig. 2a, rhythmic and coordinated leg swing motions are generated by an entirely pneumatic ring oscillator70. More specifically: Input encoding: in this robot, a small pressure tank supplies a constant pressure (P+) to drive the analog oscillator. Kernel: the oscillator circuit is the kernel. The three soft valves inside this circuit serve as inverters with a built-in delay, and a snap-through membrane alternates between closed and open flow paths, allowing the high-pressure flow to advance around the ring. This mechanism essentially transforms the steady input into a phase-shifted sequence of pressure pulses at the three nodes. Output decoding: the pulsed output pneumatic pressure from the oscillator flows to the corresponding soft legs, which convert the pressure inflation into mechanical swing motions for walking. Re-programming: in addition, a soft bistable valve and tethered mechanical controller are added to swap two connections like a latching switch, so triggering the valve can reverse the output pressure pulse sequence, thus reversing the locomotion direction. a Electronics-free pneumatic control: a soft ring-oscillator circuit generates rhythmic leg actuation from a constant pressure input, enabling a quadruped to produce diagonal-couplet walking gaits. A bistable 4/2 switch selects gait direction, and dual oscillators set the phase between leg pairs (image adapted from ref. 70 with permission). b Controller-free SMA modular robot: a curved mono stable beam and a mechanical slider can convert a single DC power supply into sustained self-oscillation. And a bistable switch can alternate power supply between the front and back modules for out-of-phase deformation and crawling (adapted from75, CC BY 4.0). c Twisted LCE ribbon robot: the ambient heating drives continuous self-rolling of this robotic structure for locomotion. When the robot contacts an obstacle, it will store elastic energy and then snap to reverse its direction, enabling autonomous avoidance and maze escape (adapted from ref. 72, CC BY-NC-ND 4.0). Note that all scale bars are approximate. Besides the fluidic circuitry, analog oscillation can also be achieved using other physical principles and material selections (Fig. 2b, c). For example, one can exploit mechanical instabilities and clever geometric design to generate motion with a constant power input, as seen in twisted LCE ribbons and architected structures that exploit snapping or buckling for autonomous rolling or twisting71,72. Similarly, beetle-like robots use spiral-shaped PVDF materials to generate mechanical resonance and rhythmic motion for insect-scale and high-speed crawling62,73,74. One can also use thermal or mechanical loops—such as SMA-actuated systems with built-in mechanical switching or microfluidic logic circuits—to generate self-sustained rhythmic actuation without digital controllers75,76,77. Challenges and opportunities of analog oscillators: analog oscillators are simple yet robust. They can tightly integrate with the soft robot’s body to generate locomotion without additional electronics. However, analog oscillators can suffer from programmability and scalability constraints: their dynamics are hard-wired into physical design. That is, the oscillator geometry, mechanical architecture, and constitutive material properties fully determine the output frequency and phase pattern. As a result, “programming” the kernel’s input-output evolution might require re-design rather than a straightforward parameter tuning. In addition, as the number of oscillators increases, fabrication tolerances and material variability can introduce mismatches that degrade synchronization. One can address these limitations by integrating the oscillators with other, more easily programmable components (e.g., combining the oscillator with fluidic logic gates as we show later in Section “algorithmic physical computing and mechano-logic”) and using high-precision manufacturing techniques at the smaller physical scale (as we discuss later in Section “perspective for future advancement”). While an analog oscillator provides a promising alternative to micro-controllers for computing and generating rhythmic motions, its information processing capability is largely embedded in its physical architecture. Recent work has shown that architected mechanical and metamaterial-based systems can support multiple motion sequences through controlled switching of actuation frequency78,79,80, rather than through real-time algorithmic control. Nevertheless, the space of attainable behaviors in these systems remains discretely prescribed by design and reconfiguration pathways. In contrast, there is an emerging notion of informational embodiment, where the combinatorial richness of a soft robot’s body deformation encodes spatiotemporal patterns without relying on symbolic or centralized representations81. This concept bridges soft-body dynamics and computation, offering another pathway toward decentralized, analog computing. Aligned with this, PRC offers a rigorous framework to formalize the soft robotic body as a computing kernel. In PRC, the body functions as a nonlinear, high-dimensional, and transiently stable dynamical system that maps input streams into distinguishable physical states. In other words, the soft body serves as a “physical recurrent neural network,” and its rich dynamics can substitute the digital recurrent neural network for temporal information processing. As we discussed in Section “analog oscillators and rhythmic motion”, a physical system truly computes only when it is intentionally used to compute abstract functions through defined input encoding and output decoding11. PRC satisfies this criterion by treating the mechanical body as a fixed, task-agnostic kernel, with only the output readout layer trained—typically via linear regression10. Compared to traditional artificial neural networks, the simplicity of treating the physical body as the computing kernel offers significantly lower computational cost, reduced memory and energy demands, and fast training—enabling its deployment in computation-embodied autonomous systems82. There are two rigorously developed frameworks for using PRC kernels. They have been demonstrated in a damped-mass-spring network10,31, and they can guide the use of PRC in soft robots. The first framework is open-loop, in which the mechanical system acts as a fixed nonlinear kernel, and only a static linear readout is trained to process the temporal data streams10. The second framework is closed-loop, in which the reservoir’s outputs are fed back to the actuators to shape future inputs, thus stabilizing or switching physical computing under simple static feedback31. Building on these two frameworks, one can deploy the open-loop PRC into robots for information perception—i.e., extracting and decoding meaningful information from the high-dimensional body dynamics. On the other hand, the closed-loop PRC can be used for embodied control—i.e., routing the reservoir computing outputs as control commands to the actuators, thus producing and modulating rhythmic body motions. In the following two subsections, we detail the working principles and applications of these two frameworks. An example of open-loop reservoir applied to soft robots: the open-loop framework allows PRC to enhance the perception of soft robots by transforming their bodies into multi-modal computing sensors. A compelling demonstration of this is the modular manipulator equipped with SMA coil actuators and simple strain gauges83 (Fig. 3a). The manipulator’s nonlinear body dynamics serve directly as the source for PRC. More specifically: Input encoding: when the manipulator grasps and lifts different payloads, its SMA actuators generate pulsed forces to “wobble” the body slightly. Kernel: in this robot, the soft body itself is the kernel (or reservoir). As the SMA wobbles the manipulator and its payload, the resulting body vibration, denoted as si(t), is captured by the strain gauges. Such a vibrational response is rich and nonlinear, so its spatiotemporal feature contains information about the weight and orientation of the payloads. Output decoding: by performing a simple and analog weighted linear summation of these strain gauge readings (O(t) = w0 + ∑wisi(t)), the robot can directly estimate the payload weight and orientation, thus classifying them. Re-programming: the readout weights wi in the output layer are trained by regression methods, which can be adjusted according to the particular computing task at hand. This example clearly illustrates how the open-loop reservoir computing framework enables soft robots to conduct spatiotemporal filtering through their intrinsic deformation dynamics, allowing them to extract complex information without requiring dense sensor arrays or extensive digital processing. As a result, numerous studies have emerged utilizing different soft robotic platforms. For instance, a fabric-based soft manipulator can estimate joint bending angle and payload weight simultaneously using only a few distributed pressure sensors84. Contact dynamics in a soft arm allow for tactile sensing and object property estimation without electronic skins85. For environment monitoring, a brush-like flexible sensor encodes surface textures through passive contact86(Fig. 3b, up), while in aerial applications, a flapping-wing robot detects wind direction directly from wing deformation, eliminating the need for airflow sensors87(Fig. 3b, middle). A SMA reservoir88 demonstrates the capability of predicting the future trajectory of its end effector under various driving signals (Fig. 3b, bottom). Collectively, these demonstrations show that soft robots with sparse, low-dimensional sensors can nonetheless achieve high-dimensional perception by exploiting their own body as the physical reservoir—making PRC a minimalist yet powerful strategy for embodied sensing. a Open-loop PRC for information perception: a modular manipulator with embedded strain gauges is driven by SMA actuators. Its high-dimensional body dynamics (measured by the strain si(t)) serve as reservoir states, which can be processed with trained linear readout wi to decode and identify the payload (image adapted from ref. 83 with permission). b Other examples of information perception with open-loop PRC, including terrain classification(image adapted from ref. 86 CC BY-ND 4.0), wind detection on a compliant membrane wing (image adapted from87, CC BY 4.0), and a self-sensing shape memory alloy actuator that could predict its end effector trajectory (image adapted from88 with permission). c Closed-loop PRC for embodied control: a quadruped robot uses its compliant spine as the reservoir. The four outputs of the reservoir kernel are fed back to the leg actuators to generate robust and adaptable locomotion gaits (image adapted from ref. 89 with permission). d Other examples of control embodiments with closed-loop PRC, including manipulation with a multi-segment continuum arm (image adapted from ref. 91 with permission) and a surface-swimming robot (adapted from ref. 94, CC BY 4.0). Note that all scale bars are approximate. An example of closed-loop reservoir applied to soft robots: beyond information perception, PRC enables soft robots to autonomously generate periodic and robust motions by embedding control into their intrinsic body dynamics. That is, instead of connecting robotic actuators to external digital controllers, one can feed the body reservoir’s output back to these actuators for real-time motor behavior control. In this case, the deformation dynamics of the robotic body and its interaction with the environment play a critical role. A representative example is the quadruped robot with its flexible spine serving as the reservoir computing kernel89(Fig. 3c). More specifically, Kernel: in this robot, its flexible spine is the kernel (or reservoir). Its intrinsic body dynamics are rich and nonlinear, capable of projecting input signals into a high-dimensional state vector. Input encoding and output decoding: in this case, the linear readout layer performs weighted linear summations, mapping the internal force and strain of the flexible spine into four control commands, each for a motored leg. The readouts are first trained in an open-loop setup with teacher forcing. Once the training is complete, the loop is closed. As a result, the reservoir computer’s output is also the input, eventually creating a self-sustained locomotion gait. Re-programming: once the loop is closed, and the robot can perform trotting, bounding, or turning—with a strong ability to recover from disturbance—simply by switching the readout weights. This embodied controller demonstrates how PRC converts compliant mechanics into an energy-efficient control system: the body remains unchanged, only the readout is “programmed,” and feedback routes it from perception to action. This closed-loop, reservoir-enabled control principle has been successfully implemented across multiple platforms, including soft silicone arms90,91(Fig. 3d), tensegrity robots92, and origami-inspired machines93, all demonstrating motor primitives and robust dynamic behaviors. A pneumatic soft robotic arm, for instance, learns different end-effector trajectories and autonomously recovers from disturbances by exploiting its intrinsic dynamic richness91(Fig. 3d, up). More recently, soft robots have also been shown to switch behaviors under varying environmental conditions by using reservoir systems that simultaneously encode control and sensory feedback94(Fig. 3d, bottom). Collectively, these demonstrations show that PRC not only simplifies and stabilizes motion generation but also enables behavior switching through embodied computation, offering an energy-efficient alternative to conventional digital control architectures. Challenges and opportunities of analog physical reservoirs: PRC offers an appealing framework to embody computation directly into soft robotic systems. In this framework, one can directly “multipurpose" a soft robotic body into a reservoir without substantial redesign, and quickly switch the computing function by adjusting the readout weights. However, reservoir computing also faces several fundamental challenges. The first is repeatability. Real-world physical systems can not guarantee identical dynamic output across multiple experiments; slight variations in fabrication, boundary conditions, temperature, and material behavior, along with drift and aging, can shift the reservoir’s dynamic responses and degrade computing performance. A second challenge is noise, as real hardware inevitably introduces sensing and actuation noise that can be amplified by the reservoir’s nonlinear dynamics, leading to degraded or even unstable output. The third challenge involves scaling. One can always increase the number of reservoir states to improve performance, but it means more sensors, more wires, and heavier data-processing burdens. Possible improvements across these areas include better operating-point stabilization and calibration procedures, noise-aware training with improved sensing electronics, and more efficient sensing architectures or dimensionality-reduction strategies that capture the essential physical dynamics without overwhelming the hardware. These efforts collectively point toward more reliable, robust, and scalable PRC systems for soft robotics. Unlike analog computing, an algorithmic computer uses abstract logical rules to drive the input-output evolution. Correspondingly, their input and output signals are typically in a discrete format (e.g., binary 0–1, on-off, or true-false bits). Our omnipresent, CMOS-based digital computers are built almost exclusively on an algorithmic architecture, relying on binary data streams passing through nested Boolean logic gates to perform computations44. However, one can also achieve algorithmic computing without electronics14. That is, instead of electrons flowing through binary logic gates, one can construct binary components that operates with elastic deformations, fluid flows, or other physical stimuli. Each physical component can act as an equivalent to logic gates, memory cells, or timing elements to fulfill computation roles. Though fundamentally different in shape and format, the underlying goals of digital and physical algorithmic computers remain the same: to perform computation tasks by following programmed logical rules for information processing. It is worth highlighting the important role of bistable mechanisms in physical algorithmic computing because they can directly emulate the 0–1 binary states of CMOS electronics. Bi-stability—defined as a physical construct’s ability to settle into two distant stable equilibria without additional external aids—arises from material or geometric nonlinearities and can be implemented using curved elastic beams95,96, elastomeric membranes97,98, or origami folds12,99,100. A bistable mechanism naturally exhibits large and rapid deformation when it snaps between its stable equilibria101, thereby amplifying the actuation output and simplifying the control of the soft robot102. For example, snapping elastic caps convert slow inflation into explosive jumping103, bistable curved fins enable fast, high-efficiency swimming104. They can also help program the robotic deformation, like in the soft sheets with an array of snap-through domes105. A bistable mechanism can also perform sensing and, therefore, encode inputs into the physical computer. For instance, a skin-like sensing surface with localized snap-through cells can act as a mechanical signal amplifiers that translate pressure or contact into discrete mechanical states106. Soft mechanosensors based on bistable structures can provide binary contact information without continuous electrical feedback107 Most importantly (and most relevant to this paper), the bistable mechanism can serve as a mechanical analog to transistors, functioning as one-bit memory units by switching between two stable configurations. These configurations can be mapped to binary states “0” and “1” based on the input force, pressure, or displacement, enabling the construction of logic gates and sequential logic circuits using entirely mechanical components14,108,109,110,111. Therefore, mechanical bi-stability provides a fundamental means to encode, store, and process information within a robot’s physical body. Typically, these robotic algorithmic computers directly borrow the design and architecture of CMOS-based systems, but they can be inherently energy efficient, retaining their state without continuous power input. To better illustrate the working principle, we detail a fluidic and algorithmic computer based on a reprogrammable metamaterial processor (Fig. 4a). The processor comprises identical bistable unit cells whose elastomeric chambers snap at defined pressure thresholds, converting vacuum and atmospheric pressure input into binary states112. As a result, a bistable unit cell with a clever tubing design can function like a resistor, enabling the construction of complex logical circuitry. More specifically Kernel: in the example, the kernel has 24 unit cells that are connected into a soft processor, including two SR latches, a 2–4 demultiplexer, and four ring oscillators (each linked to a soft robotic finger). The soft processor and the fingers are all powered by one constant vacuum pressure. The entire system can reversibly switch between four different operation modes, each of which corresponds to the oscillatory bending of one finger. Input encoding: the operator can choose the operating mode via manually pressing the input cells of the SR latches (input encoding). For example, if the first figure is activated initially, one can press the “S2” cell to activate the second figure and then press the “R2” cell to switch back. The outputs of the two SR latches are sent to the demultiplexer, so these 2 data lines are converted into 4. The resulting four outputs of the demultiplexer serve as the power source for the ring oscillators. Output decoding: the robotic fingers transform the output oscillatory pressures into mechanical bending. Notably, the current operation mode persists even after the removal of the pressing force, owing to the ability of the SR latches to retain their logic states until updated by new inputs. Re-programming: finally, one can re-arrange these fluidic unit cells to construct a new soft processor with different input-output mapping. a Reprogrammable metamaterial processor with robotic fingers: fluidic unit-cells with 0–1 binary states are connected to create mechanical logic circuitry to control finger actions (adapted from ref. 112, CC BY 4.0). b Complementary soft pneumatic valves: Piston-based, four-terminal modules are paired to achieve Boolean logic operation, nonvolatile latches, and analog pressure regulation. Then they are integrated with sub-circuits to create ring oscillators and counters to control crawling robots and wearable devices (adapted from ref. 114, CC BY-NC-ND 4.0). c Soft-matter computer: conductive-fluid receptors transduce spatiotemporal fluid patterns into electrical drives, which can realize analog filtering, amplification, and logic gates with simple composition. As a result, such conductive fluidic mechanism enables on-body control for locomotion, reflexive grasping, and behavior switching (image adapted from ref. 121 with permission). Note that all scale bars are approximate. In addition to the example above, there are several other attempts to copy the electronic logic circuitry into the fluidic domain and construct fluid Boolean gates with soft complementary valves, ring oscillators, and modular cells113,114,115,116,117,118,119 (Fig. 4b). Besides the pressurized fluidics, algorithmic computing can also be implemented with other novel materials and multi-stable mechanisms. Here, we list four additional approaches: (1) conductive fluidics: conductive fluidic receptors (CFRs) embedded in soft structures can act as hybrid mechanical-electrical logic units, enabling soft matter computers to perform sensing, logic, and actuation all in one continuous system120,121,122 (Fig. 4c). (2) Magnetic fluidics: magnetic liquid metal droplets can create flexible and reconfigurable logic gates with decoupled input/output channels and multi-modal control using phase-state transitions123. (3) Heat responsive materials: mechanical logic has also been achieved using mechanical and multiplexed switches that integrate bistable beams and thermally responsive materials to perform logic operations and mechanical memory storage112,124,125,126. (4) Multi-stable mechanisms: finally, algorithmic computing is also possible with pure elastic force and deformation. Unique architectures, such as counter-snapping metamaterials, provide logic behavior via geometric nonlinearity, where structural instability enables programmable stiffness transitions and collective switching sequences, making them useful for timing and computation21. Recent advances in modular chiral origami metamaterials further expand this logic repertoire by introducing multi-stable and reprogrammable architectures that can store information through mechanically encoded hysteresis and noncommutative state transitions127. It is worth noting that physical algorithmic computing can also enable locomotion generation and sequencing— locomotion turns out to be the robot task shared by all physical computers reviewed in this study. In this regard, algorithmic computing supports locomotion sequencing through timing control and built-in periodicity. For example, pneumatic ring oscillators and fluidic valve networks, constructed from bistable logic gates, have been used to generate self-sustained actuation cycles for crawling and walking gaits in soft quadrupeds and hexapods114,116,117. Morphologically encoded logic and routing delays, utilizing internal resistance gradients, have also been employed to produce pressure wave propagation and staggered motion, enabling gait generation through a single input channel128. Reconfigurable metamaterials and origami systems offer structural ways to embed sequencing. For instance, modular soft metamaterial robots have been programmed to switch between gaits—turning, serpentine, reciprocating—by physically re-arranging submodules acting as logic units112,117. In another case, origami robots with memory registers and rotating read-heads perform controlled motion paths by storing finite-state instructions mechanically124. Challenges and opportunities of physical algorithmic computing: compared to analog physical computers, algorithmic physical computing is quite versatile in that it can borrow many designs and working principles from well-established CMOS electronics. However, the reviewed examples above lag behind in terms of speed and scaling. Their computing speed is constrained by the relatively slow physical processes, such as pressurizing and venting of fluidic networks, deformation of thick elastomeric chambers, and, in some cases, heat diffusion through responsive materials. Using physical signals instead of electric ones also makes miniaturization more challenging: The finite size of multi-stable unit cells, the need for compliant interconnection devices, and the risk of mechanical crosstalk between unit cells make routing and isolation harder than in CMOS. Therefore, significant research efforts are necessary. For example, using advanced manufacturing technology can help minimize the unit cell size and enable more integrated packaging, thus speeding up physical computers (as we discuss later in Section “perspective for future advancement”). Regardless, physical algorithmic computing is still a desirable choice for small-scale logic and simple on-board control (as we discuss further in the conclusion section). Since this perspective lies at the intersection of physical computing and soft robots, it is intuitive to ask questions about the future direction using the “supply-and-demand” analogy. On the supply side: “are there any newly available capabilities in physical computing that can be deployed for soft robotics?” On demand side: “what additional computing power would future soft robots require?” Here, our unique perspective of dissecting soft robots into the encoding, kernel, and decoding layers can offer a systematic framework to introduce new computing concepts. For example, one can keep the encoding (e.g., sensor and input) and decoding components (e.g., actuator) the same, and “swap” the kernel with different designs that have new kinds of computing capacity. Alternatively, one can “upgrade” the kernel with a more advanced design. By surveying the current physical computing studies, one can discover many unique approaches that could be integrated into soft robots in the future (examples in Fig. 5). One can continue to advance the computing kernel’s density and capacity in soft robots by adopting new strategies to process encoded inputs, either locally or through a centralized kernel. To illustrate some of the promising concepts, we envision an octopus-inspired soft robot with computational capabilities distributed across its tentacles, as well as in its brain, following the mechanical computing framework described in this manuscript. Each component can encode, process, and decode data (with mechanical memory reserved for storage). Starting from the top right tentacle and moving clockwise: bistable soft shells enable rule-changeable logic operations (image adapted from ref. 137 CC BY 4.0); information processing during transmission via nondispersive mechanical solitary waves (image adapted from ref. 139 CC BY 4.0); a mechanical neural network offloads computation and can be attached to the robot’s skin (image adapted from ref. 146 with permission); mechanical analog-to-digital converters can be embedded in the tentacles (image adapted from ref. 33 with permission). In the robot’s brain, miniaturized physical circuits mimic an algorithmic logic unit (ALU) (image adapted from ref. 18 CC BY 4.0). Finally, reprogrammable and nonvolatile mechanical memories can store data either with magnetic (left, image adapted from ref. 134 with permission) or thermal principles (right, image adapted from ref. 132 CC BY 4.0). Note that all scale bars are approximate. Here, we highlight three most promising topics from three different angles: function, scale, and system integration. Regarding the function, the current physical computing has demonstrated an impressive ability to extract information from sensory signals and execute actuation commands. On the other hand, on-board nonvolatile memory, a vital component in modern computing paradigms, has yet to be implemented in soft robots. Regarding scale, the current physical computing in soft robots is relatively large in terms of physical size. Minimizing their scale using high-precision manufacturing techniques might help advance the performance and reliability of physical computers to a new level, addressing some of the challenges in physical computing as we discussed earlier. Finally, regarding systematic integration, the current physical computing setup in soft robots primarily operates in a standalone manner. However, some integration with digital hardware (e.g., for long-distance communication) can enhance the overall capability. Therefore, a meaningful and integrated mechanical-electrical hybrid circuit can be advantageous. In the following section, we present recent studies in these three aspects. An advanced and autonomous robot should also be able to memorize the operator’s instructions or the knowledge from its interaction with the working environment. In this regard, we have seen some promising examples of memory in the mechanical metamaterial domain. That is, by combining responsive materials (memory encoding/decoding) and elastic bistability (storage), mechanical metamaterials can achieve information storage via mechanical bits (m-bits), similar to their digital counterparts. These m-bits can be bistable elastic shell to realize mechano-fluidic memory129, or bistable origami structures130 or tiles of bistable Kirigami units131, forming 2D and 3D storage arrays. Examples include temperature-responsive bistable Kirigami units132,133, which sequentially retrieve stored information in 3D arrays, and magnetic-responsive bistable elastic shell units134, which enable on-demand re-programmability for 2D arrays. These configurations function as nonvolatile mechanical memories and could be used in soft robotics in the future. A key challenge will be designing an integrative approach to encode information from the physical memory into the computing kernel, and subsequently decode and store the computing output into the physical memory device. Just as CMOS-based computers’ never-ending quest to shrink the size of their basic electronic units, soft robots can also benefit from a smaller and more capable physical computer onboard. Early physical algorithmic computers —such as the waterbomb origami with bistable hinges135 — were bulky and limited to AND, OR, and NOT gates. More compact designs like bistable curved beam arrays have introduced NOR and NAND operations136. Bistable soft shells allowed re-programmable mechanologics so that their operation mode can be switched on demand (e.g., from XOR to XNOR)137. Additionally, self-powered origami mechanologics19 and thermal mechanical transistors24 completed the binary logic set with XNOR and XOR gates. More recently, with advancements in sub-millimeter additive manufacturing, small-scale mechanologics based on buckling micro-flexures have emerged18,138. Integrating these mechanologics has led to fully mechanical half-adders19,136,137,138, full-adders19,24,137, and solitary wave-based mechanical computing platforms139. It is not hard to imagine that some of these miniaturized and physical algorithmic units will be integrated into soft robots in the future, enabling fully onboard computation and control. While physical computing aspires to perform computation tasks without complex CMOS-based electronics, it can still benefit from using some simple electronic components. Indeed, the physical reservoir computer reviewed in this study is a mechanical-electric hybrid system, because its readout layer requires electronics to perform weighted linear summation (e.g., using an analog adder circuit with an Op-Amp). This provides the physical reservoir with excellent re-programmability and multi-tasking abilities that are not yet available in analog oscillators or physical algorithmic computers. Therefore, mechanical-electrical hybrid circuits could provide scalable computing capabilities for future soft robots. These hybrid systems convert mechanical input into electrical signals by opening or closing conductive pathways in response to deformation. Applications include mechanical digital sensors140, mechanical analog-to-digital converters (m-ADC)141, and mechanical arithmetic logic units (m-ALU), which embed Boolean logic into soft configurable structures33,142,143. Higher-level computation becomes feasible when memory is integrated into computation, as seen in in-memory mechanical computing platforms, such as mechanical neural networks17 and linear equation solvers144. These hybrid circuits have yet to be integrated into soft robotics, but they present an exciting pathway for systematic integrations. In summary, the convergence of physical computing with soft robotics is a promising strategy for softening and onboarding control. By integrating a physical computing kernel—such as an analog oscillator, a physical reservoir computer, or an algorithmic computer—into a soft robotic system, these robots can achieve sophisticated locomotion and manipulation tasks that would typically require a conventional digital control. For example, this perspective paper demonstrates that an electronics-free legged robot equipped with an analog oscillator can perform coordinated locomotion and reverse direction upon encountering an obstacle. The soft modular manipulator, with inherent PRC capacity, can utilize its body dynamics to estimate the weight and orientation of its payload, enabling it to classify the payload without relying on electronic sensors, such as cameras. Another soft robotic hand integrated with an algorithmic fluidic circuitry that can operate based on abstract Boolean logical rules. However, it is worth noting that all of the physically computing robots in this perspective remain at the proof-of-concept level. Implementing these exciting concepts into practical, real-world use still requires a significant amount of research efforts and system engineering. Despite the rapid advances in this field, it is unlikely that physical computers embedded in soft robots can catch up with digital hardware in terms of computational speed and information density in the foreseeable future. Therefore, it is unrealistic to replace conventional digital hardware entirely with physical computing. Instead, engineers must answer the critical question: “how much should we use physical computing?" “Where to apply them?" and “how can we seamlessly integrate physical computing with conventional digital hardware?" For robots, physical computing is advantageous because of its softness, simplicity, and robustness. Therefore, it makes the most sense to use physical computing in the following three scenarios. (1) The targeted tasks are closely related to the robot’s physical body—this is why we have seen great success in locomotion generation and information extraction via direct physical interaction (using PRC). (2) The robots need to be small and entirely soft—because physical computing could seamlessly integrate with the soft robotic body without the complexity of adding electronic components (e.g., using advanced 3D printing). (3) The working conditions are demanding—for example, fluidics-based computation is desirable for underwater operations, where electronics are vulnerable to damage. On the other hand, conventional digital electronics is more suitable for “over the distance” tasks, such as obstacle avoidance using vision data or long-distance communication with operators. Therefore, the future of physically computing robots hinges upon two pillars: the continual advances in physical computing and its strategic integration with conventional digital hardware. As we discussed in Section “perspective for future advancement”, in the foreseeable future, we are likely to witnessf the creation of more powerful physical computing thanks to miniaturization and integrated memory capacity. 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Article ADS Google Scholar Download references The authors acknowledge support from the National Science Foundation (CMMI-2312422, 2328522, EFRI-2422340) and Virginia Tech (via the Startup Fund and a Graduate Student Assistantship). These authors contributed equally: Jun Wang, Ziyang Zhou, Ardalan Kahak. Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA Jun Wang, Ziyang Zhou, Ardalan Kahak & Suyi Li Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar J.W., Z.Z., and A.K. contributed equally to this work and jointly conceived the core ideas, reviewed the relevant literature. They collaboratively developed the figures and prepared Sections 2–5 of the manuscript. S.L. supervised the research, guided the conceptual framework, wrote the introduction and summary, secured funding, and contributed to manuscript editing and final approval. Correspondence to Jun Wang or Suyi Li. The authors declare no competing interests. Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Wang, J., Zhou, Z., Kahak, A. et al. Embodying physical computing into soft robots. Nat Commun 17, 2455 (2026). https://doi.org/10.1038/s41467-026-70866-6 Download citation Received: 12 October 2025 Accepted: 06 March 2026 Published: 15 March 2026 Version of record: 16 March 2026 DOI: https://doi.org/10.1038/s41467-026-70866-6 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. 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Le Prime Day d’Automne réserve cette année de belles surprises à ceux qui souhaitent s’équiper en robots de nettoyage dernier cri. Narwal, leader mondial des robots aspirateurs intelligents, propose des offres irrésistibles. Découvrez comment profiter de ces promotions exclusives et des innovations qui facilitent vraiment le quotidien. Pour le Prime Day, Narwal offre jusqu’à 720 € d’économies sur ses modèles phare. Ces remises sont disponibles du 7 au 12 octobre sur Amazon et sur la boutique officielle de la marque. Les clients peuvent choisir parmi plusieurs robots avec différentes fonctionnalités avancées. En prime, les packs exclusifs sur le site incluent deux ans d’accessoires supplémentaires. Il s’agit d’une occasion unique d’acquérir un robot intelligent à un prix imbattable. La gamme Narwal brille par sa technologie et sa facilité d’utilisation. Parmi les modèles en promotion : Tous les modèles intègrent des technologies pratiques comme le lavage automatique, la stérilisation des serpillières ou l’évitement intelligent des obstacles. Ainsi, ils répondent aux besoins des familles modernes et actives. En plus des offres, Narwal propose un jeu concours exclusif pour le Prime Day. Il permet de gagner des robots Freo Z10, des coffrets cadeaux ou d’autres lots étonnants. Chaque participant repart avec un cadeau, ce qui renforce l’expérience client. Pour jouer, il suffit de se rendre sur le site officiel Narwal et de suivre les instructions pour tenter sa chance. Grâce au Prime Day Narwal, il n’a jamais été aussi facile d’équiper son foyer avec des robots intelligents et de profiter d’une maison toujours propre. Entre réductions importantes, accessoires offerts et jeu concours, Narwal s’impose comme la référence de l’entretien du sol pour un quotidien plus simple et agréable. Saisissez ces offres pour découvrir les avantages des robots aspirateurs nouvelle génération ! Lisez notre dernier article tech : Test – AAWireless Two : l’adaptateur Android Auto sans fil qui libère vos trajets Votre adresse e-mail ne sera pas publiée. Les champs obligatoires sont indiqués avec * Commentaire * Nom * E-mail * Δ

SAN JOSÉ, Calif., March 19, 2026 /PRNewswire/ — Universal Robots (UR) this week unveiled the UR AI Trainer at GTC 2026. Developed in collaboration with Scale AI, the AI Trainer marks a shift as robots move from pre-programmed applications to fully AI-driven tasks. “Our customers, ranging from large enterprises to AI research labs, are no longer just asking for AI features,” said Anders Beck, VP of AI Robotics Products at Universal Robots. “They need a way to collect high-fidelity, synchronized robot and vision data to train AI models on the same robots they intend to deploy. Our AI Trainer is the industry’s first direct lab-to-factory solution for AI model training.” Enabling AI-ready data capture AI robotics training is often hindered by fragmented hardware and low-fidelity data capture. Today’s training data is collected on research robots not suited for production environments, and many systems rely only on visual feedback, making delicate or contact-rich tasks difficult. “The AI Trainer directly addresses these barriers,” said Beck. “By utilizing our unique Direct Torque Control and force feedback features, we give developers direct influence over how the robot physically interacts with the world, training on the same robust hardware used in over 100,000 industrial deployments.” Scale AI partnership enables a flywheel of integrated robotics data The UR AI Trainer lets human operators guide UR robots through tasks in a leader-follower setup, capturing high-quality synchronized multimodal data during real-time demonstrations, creating the structured datasets needed to train Vision-Language-Action (VLA). Running on UR’s AI Accelerator platform, the AI Trainer combines collaborative industrial robots with Scale AI software to enable scalable data capture in production environments, supporting continuous optimization of physical AI systems. “Universal Robots is a leader in industrial robotics, and its global footprint offers the ideal foundation for data capture and AI deployment,” said Ben Levin, General Manager, Physical AI at Scale AI. “Together, we’ve created an integrated robotics data flywheel, allowing customers to train, deploy, and improve their AI models faster than ever before.” UR and Scale AI will release a large-scale industrial dataset collected on UR robots later this year. Experience AI Trainer at GTC Visitors to UR’s GTC booth can guide two UR3e ‘leader’ robots providing haptic input to control two UR7e ‘follower’ robots. The setup enables visitors to perform advanced smartphone packaging with haptic feedback for imitation learning and VLA training, with demonstration data recorded in real time on Scale’s stack and replayable directly on the AI Trainer. The process of capturing robot training data for AI models is complemented by an embodied foundation model demo with Generalist AI and a haptics-based training demo with Haply Robotics. Read more on the UR website. See image collection here. About Universal Robots is a global leader in collaborative robotics (cobots), used across a wide range of industries. With over 100,000 cobots sold worldwide, our user-friendly platform is supported by intuitive PolyScope software, award-winning training, comprehensive services, and the world’s largest cobot ecosystem, delivering innovation and choice to our customers. Universal Robots is part of Teradyne Robotics, a division of Teradyne (NASDAQ: TER), a leading supplier of automatic test equipment and advanced robotics technology. Scale AI‘s mission is to develop reliable AI systems for the world’s most important decisions. We provide high-quality data that powers the world’s AI models, and we help enterprises and governments build, deploy, and oversee AI applications that create real impact. Through our research and Safety, Evaluations, and Alignment Lab (SEAL), we test models with rigorous benchmarks and novel research to help ensure AI is developed in ways people can trust. Founded in 2016, Scale is headquartered in San Francisco. View original content:https://www.prnewswire.com/apac/news-releases/universal-robots-and-scale-ai-launch-imitation-learning-system-to-accelerate-ai-model-training-bridging-the-lab-to-factory-gap-302717348.html SOURCE Universal Robots ​ Your email address will not be published. Required fields are marked * Comment * Name * Email * Website Save my name, email, and website in this browser for the next time I comment. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved Login to your account below Remember Me Please enter your username or email address to reset your password. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved

We encourage you to review our Terms of Service, and Privacy Policy. By continuing, you agree to the Terms listed here. In case you want to opt out, please click "Do Not Sell or Share My Personal Information" link in the footer of this page. We won't sell or share your personal information to inform the ads you see. You may still see interest-based ads if your information is sold or shared by other companies or was sold or shared previously. Interested in blogging for timesofindia.com? We will be happy to have you on board as a blogger, if you have the knack for writing. Just drop in a mail at toiblogs@timesinternet.in with a brief bio and we will get in touch with you. Somjit Amrit, stumbled upon the world of blogging. Reading is something he enjoys, a sort of second nature to him. The constructive corollary of reading is writing. A decade ago, he started writing reviews of the books, read. Subsequently, he started blogging on a variety of subjects: lessons learned from Mother Nature, first-hand experiences of relevance to help others, and technology for business are some of the areas of his interest. With over 30 years of rich professional experience, having led global business units spanning 4 continents, in the IT Services industry, he is the CEO of IIT Mandi iHUB and HCI Foundation which is one of the 25 Technology Innovation Hubs in the country sponsored by the Government of India. He is an engineer by qualification with a management degree from IIM Lucknow. He enjoys setting the “rake-through-the-hair” questions for quiz competitions. He is attempting to perfect a recently discovered natural talent in musical whistling as a stress buster. Can he pick up the harmonica this year as the grand resolution for the year? He will surely update you. LESS ... MORE Humanoid robots look convincing on stage or curated social media forwards. They walk, pick up objects, and in some demonstrations, they even smile and converse. This creates the expectation that machines will soon behave like humans. In practice, however, most humanoid platforms excel at isolated capabilities but struggle in continuous, unscripted social and physical interaction. They may drop objects, misinterpret gestures, mis-time responses, or pause when faced with noisy sensory input. These limitations reveal a deeper truth: building a humanoid robot is not about perfecting any single component. It is about closing tightly coupled loops between perception, reasoning, and action across multiple modalities. Multimodality is the structural solution to this problem. Human interaction is a tightly coupled stream of audio, visual, tactile and contextual signals that arrive and must be interpreted together in real time. For rigid robots to behave with human-like fluidity, their software stacks cannot treat these channels as separate pipelines that exchange occasional messages. Instead, they must build shared internal representations that are synchronized in time, fused across sensing modalities, and available both to perception modules that infer intent and to control modules that plan and execute motion. When a person points while saying, “Put it there,” the robot should align the gesture, the pointing vector, the spoken phrase, the gaze and the scene geometry in a single moment of understanding, and then generate a motor plan that respects force constraints, spatial and temporal balance, and the social context of the interaction. The Missing Link: Synchronization and Real-Time Fusion While multimodality provides the structural foundation, the real challenge lies in synchronizing and fusing these multiple sensory streams. Humanoid robots cannot achieve human-level fluency by processing visual, auditory, tactile, and contextual information independently. Each modality informs and constrains the others, and seamless integration in real time is essential for coherent decision-making. Key capabilities enabled by multimodal AI include: Synthesize Context: A robot interacting with a human, needs to combine facial expression data, speech audio, and environmental context to determine whether the person is frustrated, joking, or requesting urgent help. Adaptive Interaction: By fusing tactile feedback (object weight, texture) with visual input (object shape, location), a robot can dynamically adjust its grip or trajectory without pre-programming every possible scenario. Predictive Coordination: Multimodal fusion allows anticipatory action. For example, combining gaze tracking with speech patterns can enable the robot to act on intentions before they are explicitly verbalised. Developing these capabilities requires end-to-end multimodal neural networks that mirror human cognitive processes. Latent representations must encode cross-modal dependencies and be updated continuously to allow smooth, safe, and intelligent interaction. Without this real-time integration, humanoid robots would continue to operate with limited agility (or pronounced rigidity) and constrained social responsiveness (or visible gawkiness), regardless of how advanced their individual sensors or algorithms may be. Moving Forward: Towards Agile, Context-Aware Human-like Robots The future of AI is not merely automation; it is augmentation and interaction. To build more agile and context-aware humanoid robots, research efforts should focus on: Robust Data Fusion Techniques: Developing algorithms that fuse asynchronous, multi-sensory data into unified latent representations, rather than merely combining outputs from separate modules. Contextual Understanding Engines: Creating AI that can interpret intent, social nuance, and environmental context, enabling reliable operation in unpredictable, real-world environments. Ethical and Responsible AI: Ensuring that multimodal systems respect privacy, avoid bias, and interact safely, particularly as they begin to operate in sensitive human contexts. The current limitations of humanoid robots are not failures; they are building blocks. By investing in multimodal AI research and the Technology Innovation Hub at IIT Mandi, we are laying the foundation for fluidic humanoid robots (human-like robots) that redefine our relationship with machines. The ultimate goal is a future where the line between physical and digital, human and AI, becomes seamless. Robots will not merely act; they will perceive, reason, and interact in ways that are coherent, context-aware, and profoundly human-like while abiding with the overarching aspect of responsible and ethical AI. Views expressed above are the author's own. High desibels The Vance Dance OMG! Don’t bet on bans The 65 Lakh Question ‘Centrism isn’t nostalgia, it is survival’ So, who’s straying? Beyond the noise Rethinking stray dogs: From crisis to opportunity Swadeshi Diwali Down the drain Interested in blogging for timesofindia.com? We will be happy to have you on board as a blogger, if you have the knack for writing. Just drop in a mail at toiblogs@timesinternet.in with a brief bio and we will get in touch with you. TOI Edit Page,Voices Erratica,TOI Edit Page,Tracking Indian Communities Juggle-Bandhi,TOI Edit Page TOI Edit Page