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Robots Doing Stuff #61 | Bimanual Nut Pick and Threading This is a real customer task: one step in a longer multi-part sequence we’re training in our labs. Threading requires precise alignment, force control, and fine manipulation. These are foundational capabilities for industrial dexterity applications that are currently inaccessible... show more

Sanctuary AI

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38,039 次观看 • 1 个月前 •via X (Twitter)

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Robots Doing Stuff is back. Ready? Robots Doing Stuff #60 | Welding Policy Repetitive, quick, and precise. Automating this manufacturing welding task with Physical AI required the successful execution of all of the above in order to achieve a consistent weld quality at production-level speed. Visit to learn more about our Physical AI-enabled automation solutions. #PhysicalAI #Robotics #Automation #AI #Manufacturing #Welding

Robots Doing Stuff is back. Ready? Robots Doing Stuff #60 | Welding Policy Repetitive, quick, and precise. Automating this manufacturing welding task with Physical AI required the successful execution of all of the above in order to achieve a consistent weld quality at production-level speed. Visit to learn more about our Physical AI-enabled automation solutions. #PhysicalAI #Robotics #Automation #AI #Manufacturing #Welding

Sanctuary AI

32,839 次观看 • 1 个月前

This is what Production-Ready Physical AI looks like. Yesterday, we announced our expanded Physical AI strategy, backed by a demonstration of a complex industrial wire plugging task completed with a 99.5%+ success rate and a 2.54-second cycle time. Why is this significant? "Manipulating a flexible wire into a moving target on a live conveyor is exactly the kind of contact-rich dexterity problem that has kept tasks like this out of reach for traditional automation ... Solving it required models built around performance from day one with reliability, cycle time, and safety measured against real production benchmarks. That's the bar Physical AI has to clear to matter in enterprise environments, and it's the bar we've now cleared." — Olivia Norton, CTO at Sanctuary AI. Read more:

This is what Production-Ready Physical AI looks like. Yesterday, we announced our expanded Physical AI strategy, backed by a demonstration of a complex industrial wire plugging task completed with a 99.5%+ success rate and a 2.54-second cycle time. Why is this significant? "Manipulating a flexible wire into a moving target on a live conveyor is exactly the kind of contact-rich dexterity problem that has kept tasks like this out of reach for traditional automation ... Solving it required models built around performance from day one with reliability, cycle time, and safety measured against real production benchmarks. That's the bar Physical AI has to clear to matter in enterprise environments, and it's the bar we've now cleared." — Olivia Norton, CTO at Sanctuary AI. Read more:

Sanctuary AI

28,759 次观看 • 2 个月前

Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

mimic

117,316 次观看 • 29 天前

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

383,765 次观看 • 1 年前

🚨 BREAKING: Swiss startup mimic just raised €13.8M to teach robots the one thing they still struggle with human-like dexterity. 🇨🇭 The Zurich-based company is developing physical AI systems that allow robots to learn directly from human demonstrations. Skilled operators wear custom motion-tracking gear on real factory floors, generating data that trains robots to grasp, move, and adapt with human precision. Founded in 2024 as a spin-off from ETH Zurich, mimic pairs AI-driven dexterous robotic hands with standard industrial robot arms, skipping the humanoid form factor in favor of practical, deployable automation. The oversubscribed seed round was led by Elaia with participation from Speedinvest, Founderful, 1st Kind, 10x Founders, 2100 Ventures, and the Sequoia Scout Fund, bringing total funding to over $20M. Their technology is already being piloted with Fortune 500 manufacturers and logistics operators, where robots are learning complex manipulation tasks once reserved for humans. Why it matters: Europe is stepping up in the AI robotics race. By making dexterity scalable, mimic bridges the gap between lab breakthroughs and real factory deployment, where automation still meets its hardest challenges. As CEO Stefan Weirich put it: “We make dexterity deployable at scale, closing the gap between what AI can do in the lab and what factories actually need.” Let's go robotics in Europe! 🔥

🚨 BREAKING: Swiss startup mimic just raised €13.8M to teach robots the one thing they still struggle with human-like dexterity. 🇨🇭 The Zurich-based company is developing physical AI systems that allow robots to learn directly from human demonstrations. Skilled operators wear custom motion-tracking gear on real factory floors, generating data that trains robots to grasp, move, and adapt with human precision. Founded in 2024 as a spin-off from ETH Zurich, mimic pairs AI-driven dexterous robotic hands with standard industrial robot arms, skipping the humanoid form factor in favor of practical, deployable automation. The oversubscribed seed round was led by Elaia with participation from Speedinvest, Founderful, 1st Kind, 10x Founders, 2100 Ventures, and the Sequoia Scout Fund, bringing total funding to over $20M. Their technology is already being piloted with Fortune 500 manufacturers and logistics operators, where robots are learning complex manipulation tasks once reserved for humans. Why it matters: Europe is stepping up in the AI robotics race. By making dexterity scalable, mimic bridges the gap between lab breakthroughs and real factory deployment, where automation still meets its hardest challenges. As CEO Stefan Weirich put it: “We make dexterity deployable at scale, closing the gap between what AI can do in the lab and what factories actually need.” Let's go robotics in Europe! 🔥

Lukas Ziegler

77,152 次观看 • 9 个月前

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Sanctuary AI

53,269 次观看 • 1 年前

For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Today, we take a major stride toward making that dream a reality: Introducing Gemini Robotics 2 from Google DeepMind, the intelligence layer powering the next generation of truly adaptable robots. This major advance unlocks intelligent whole-body control, advanced dexterity, and even multi-robot collaboration 🤯. Ok but... how does a robot actually "think"? Real-world tasks take time and planning. To manage that complexity, our new embodied reasoning model, Gemini Robotics ER 2, acts as the robot’s high-level brain, enhancing the robot’s capabilities to: — Observe the environment — Reason about the actions needed to complete the task — Coordinate with the vision-language-action model to carry out actions — Track progress until the job is done This setup allows robots to execute complex multi-step workflows, self-correct if a step fails, and adapt to completely novel situations. Learn more about Gemini Robotics ER 2 (and our two other brand new models) here:

Google AI

637,461 次观看 • 22 天前

Eka means unity -- “one,” in Sanskrit and “first” in Finnish. We’re building intelligence for the physical world in its native language: forces. Until now, robotics faced a tradeoff — generality or speed. The real world requires both. Robotics also faced a data problem. Our Vision–Force–Action (VFA) model — the first of its kind — breaks the generality-speed tradeoff and the data barrier. It's a new foundation uniting performance, generality, and safety for putting capable robots in everyone's hands. Today, I am excited to share our journey of pushing robots beyond human limits. Today, dexterity becomes scalable. Today, I welcome you to the Era of Eka. Co-founded with Tuomas Haarnoja, and so thrilled and grateful to be working with a dream team at Eka Robotics, Inc.. Learn more:

Eka means unity -- “one,” in Sanskrit and “first” in Finnish. We’re building intelligence for the physical world in its native language: forces. Until now, robotics faced a tradeoff — generality or speed. The real world requires both. Robotics also faced a data problem. Our Vision–Force–Action (VFA) model — the first of its kind — breaks the generality-speed tradeoff and the data barrier. It's a new foundation uniting performance, generality, and safety for putting capable robots in everyone's hands. Today, I am excited to share our journey of pushing robots beyond human limits. Today, dexterity becomes scalable. Today, I welcome you to the Era of Eka. Co-founded with Tuomas Haarnoja, and so thrilled and grateful to be working with a dream team at Eka Robotics, Inc.. Learn more:

Pulkit Agrawal

338,567 次观看 • 3 个月前

As part of our walkthrough series, today we are covering labor in the physical AI era—why real robots and real jobs hinge on grounded data, human judgment, and a pipeline from capture to delivery, not simulation alone. If you’re following Harbor or the future of work in AI, this one connects the dots in a few minutes.

As part of our walkthrough series, today we are covering labor in the physical AI era—why real robots and real jobs hinge on grounded data, human judgment, and a pipeline from capture to delivery, not simulation alone. If you’re following Harbor or the future of work in AI, this one connects the dots in a few minutes.

Harbor ML

13,443 次观看 • 3 个月前

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

45,757 次观看 • 2 个月前

I figure we've got about 7 years left until the vast majority of human jobs today are gone. The automation cliff represents a stark departure from how we typically think about automation happening gradually. Rather than a steady staircase of incremental improvements where humans slowly hand over control to machines, we're heading toward a precipitous drop where entire industries and job categories will transition from human to automated control virtually overnight. This cliff is becoming possible because of two key developments: humanoid robots that can seamlessly replace humans in physical tasks, and AI agents that can take over knowledge work through standard computer interfaces. Unlike previous waves of automation that required custom infrastructure and careful human oversight, these technologies are "drop-in" solutions - they can simply step into existing human roles and take over completely. The transformative power comes from eliminating the need for human involvement entirely. Partial automation often performs worse than either full human control or full automation, since managing the handoffs between human and machine creates new problems and cognitive burden. We've already seen this with systems like pharmaceutical manufacturing and agricultural harvesting, where removing humans from the loop actually improved performance dramatically. While technical complexity and economics have historically prevented this kind of complete automation, advanced AI and robotics are rapidly eliminating these barriers. Once these systems exceed human capabilities in strength, dexterity, and intelligence - which is already beginning to happen - we'll see wholesale replacement of human labor across industries, potentially within the next 5-7 years. This isn't the gradual automation story we've been telling ourselves. A cliff is coming, and we need to be ready for the profound economic and social changes it will bring.

I figure we've got about 7 years left until the vast majority of human jobs today are gone. The automation cliff represents a stark departure from how we typically think about automation happening gradually. Rather than a steady staircase of incremental improvements where humans slowly hand over control to machines, we're heading toward a precipitous drop where entire industries and job categories will transition from human to automated control virtually overnight. This cliff is becoming possible because of two key developments: humanoid robots that can seamlessly replace humans in physical tasks, and AI agents that can take over knowledge work through standard computer interfaces. Unlike previous waves of automation that required custom infrastructure and careful human oversight, these technologies are "drop-in" solutions - they can simply step into existing human roles and take over completely. The transformative power comes from eliminating the need for human involvement entirely. Partial automation often performs worse than either full human control or full automation, since managing the handoffs between human and machine creates new problems and cognitive burden. We've already seen this with systems like pharmaceutical manufacturing and agricultural harvesting, where removing humans from the loop actually improved performance dramatically. While technical complexity and economics have historically prevented this kind of complete automation, advanced AI and robotics are rapidly eliminating these barriers. Once these systems exceed human capabilities in strength, dexterity, and intelligence - which is already beginning to happen - we'll see wholesale replacement of human labor across industries, potentially within the next 5-7 years. This isn't the gradual automation story we've been telling ourselves. A cliff is coming, and we need to be ready for the profound economic and social changes it will bring.

David Shapiro (L/0)

24,221 次观看 • 1 年前

What if we're approaching robotic manipulation all wrong? Ilya Sutskever makes a fascinating point here: Current approaches throw massive amounts of data at the problem — millions of simulation steps, huge compute. And still, robots can't match human dexterity. Real-world learning of new skills? "Very out of reach." Meanwhile, humans pick up manipulation tasks almost instantly. Our dexterity is unmatched. And we do it with remarkable robustness — no reward shaping, no curriculum design, no training instability. Why the gap? Evolution. 500M years of optimisation for locomotion, vision, and manipulation. For these ancient sensorimotor tasks, maybe brute-force data isn't the answer. Maybe we should be copying the biological algorithms that already solved it. CC: Dwarkesh Patel

What if we're approaching robotic manipulation all wrong? Ilya Sutskever makes a fascinating point here: Current approaches throw massive amounts of data at the problem — millions of simulation steps, huge compute. And still, robots can't match human dexterity. Real-world learning of new skills? "Very out of reach." Meanwhile, humans pick up manipulation tasks almost instantly. Our dexterity is unmatched. And we do it with remarkable robustness — no reward shaping, no curriculum design, no training instability. Why the gap? Evolution. 500M years of optimisation for locomotion, vision, and manipulation. For these ancient sensorimotor tasks, maybe brute-force data isn't the answer. Maybe we should be copying the biological algorithms that already solved it. CC: Dwarkesh Patel

Jack 🤖

31,262 次观看 • 8 个月前

🚀 We just shipped a big upgrade to our imitation-learning-in-simulation playground in LeRobot, built together with Lightwheel ! You can now teleoperate robots in sim (keyboard or real robot) and collect training demos instantly. This makes it possible to run real IL research on harder, more realistic manipulation tasks, even if you don’t have hardware. New tasks: 🟠 pick orange to the plate 🧺 fold cloth (yes!) 📦 pick 2 “e” toys to the box 🔴 lift red cube Through our partnership with Lightwheel, LeIsaac was integrated into EnvHub on day one, a best-practice integration that strengthens both ecosystems and pushes simulation-first robotics forward. 👉 Load a task via EnvHub, start teleop-ing, record your demo, upload the data on the hub and you’re ready to train or test.

🚀 We just shipped a big upgrade to our imitation-learning-in-simulation playground in LeRobot, built together with Lightwheel ! You can now teleoperate robots in sim (keyboard or real robot) and collect training demos instantly. This makes it possible to run real IL research on harder, more realistic manipulation tasks, even if you don’t have hardware. New tasks: 🟠 pick orange to the plate 🧺 fold cloth (yes!) 📦 pick 2 “e” toys to the box 🔴 lift red cube Through our partnership with Lightwheel, LeIsaac was integrated into EnvHub on day one, a best-practice integration that strengthens both ecosystems and pushes simulation-first robotics forward. 👉 Load a task via EnvHub, start teleop-ing, record your demo, upload the data on the hub and you’re ready to train or test.

LeRobot

44,647 次观看 • 8 个月前

GEN-1 ran 2 live demos across 2 different robots at Automate 2026 this year: folding and packing boxes, and servicing robot vacuums. What resonated most wasn't just that the models could do the tasks — it was that they had the intelligence to recover in real time when things went wrong, and this got people thinking differently about automation. The strongest signal came from the customer conversations around the demos. All week, manufacturers, integrators, and automation teams brought us real workflows: packaging, kitting, wiring, inspection, assembly, and more. The opportunity for physical AI to impact dexterous, real-world automation is huge, and we're only just getting started. For early access to our models, reach out 📧 partnerships@generalistai.com

GEN-1 ran 2 live demos across 2 different robots at Automate 2026 this year: folding and packing boxes, and servicing robot vacuums. What resonated most wasn't just that the models could do the tasks — it was that they had the intelligence to recover in real time when things went wrong, and this got people thinking differently about automation. The strongest signal came from the customer conversations around the demos. All week, manufacturers, integrators, and automation teams brought us real workflows: packaging, kitting, wiring, inspection, assembly, and more. The opportunity for physical AI to impact dexterous, real-world automation is huge, and we're only just getting started. For early access to our models, reach out 📧 [email protected]

Generalist

15,649 次观看 • 1 个月前

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

Runway

35,040 次观看 • 8 个月前

Everyone is racing to build smarter robots. But here’s the question: Can a robot truly understand the physical world by only seeing it? Humans don’t learn with vision alone. We learn through sound, sight, motion, and touch… all working together to make sense of our environment. That’s what makes Humyn Labs so interesting. They’re building the Multi-Sensory Platform for Physical AI, creating one fused training signal instead of isolated datasets. And through KGeN Community’s verified global network, that data comes from real people performing real tasks in real environments. In this video, I explain why I think this approach could be one of the missing pieces for Physical AI’s next leap… and why I used the Infinity Gauntlet as the perfect analogy. 👇🎥 Watch, share your thoughts, and let me know if the comparison makes sense. Humyn Labs KGeN Community KGeN Foundation 🟩 #HumynLabs #KGeN #PhysicalAI #AI

Everyone is racing to build smarter robots. But here’s the question: Can a robot truly understand the physical world by only seeing it? Humans don’t learn with vision alone. We learn through sound, sight, motion, and touch… all working together to make sense of our environment. That’s what makes Humyn Labs so interesting. They’re building the Multi-Sensory Platform for Physical AI, creating one fused training signal instead of isolated datasets. And through KGeN Community’s verified global network, that data comes from real people performing real tasks in real environments. In this video, I explain why I think this approach could be one of the missing pieces for Physical AI’s next leap… and why I used the Infinity Gauntlet as the perfect analogy. 👇🎥 Watch, share your thoughts, and let me know if the comparison makes sense. Humyn Labs KGeN Community KGeN Foundation 🟩 #HumynLabs #KGeN #PhysicalAI #AI

Bahdfella™ ❤️‍🔥

28,381 次观看 • 23 天前

Generative AI has the potential to revolutionize everything companies do. Together with Microsoft we are launching the Siemens Industrial Copilot, an AI powered assistant to supercharge collaboration between humans and machines. Across industries these will boost innovation, speed up code development and tackle the shortage of skilled labor. Already our customer Schaeffler is using our innovation to write software for industrial automation systems like robots. In this video, Satya and I talk about the partnership that created the copilots and discuss technology’s potential to revolutionize the way companies design, develop, manufacture, and operate – starting today! #AI #ArtificialIntelligence #GenerativeAI #Digitalization Satya Nadella Scott Guthrie Cedrik Neike Microsoft Cloud Siemens Schaeffler Group

Generative AI has the potential to revolutionize everything companies do. Together with Microsoft we are launching the Siemens Industrial Copilot, an AI powered assistant to supercharge collaboration between humans and machines. Across industries these will boost innovation, speed up code development and tackle the shortage of skilled labor. Already our customer Schaeffler is using our innovation to write software for industrial automation systems like robots. In this video, Satya and I talk about the partnership that created the copilots and discuss technology’s potential to revolutionize the way companies design, develop, manufacture, and operate – starting today! #AI #ArtificialIntelligence #GenerativeAI #Digitalization Satya Nadella Scott Guthrie Cedrik Neike Microsoft Cloud Siemens Schaeffler Group

Roland Busch

25,304 次观看 • 2 年前

HUMANOID ROBOTS ARE AMAZING. BUT WHERE IS THE ROI? Watching a humanoid robot work at BMW is genuinely impressive. As an engineer, I love it. But I also can’t ignore one question. For this task, why not use a 6-axis robot on a linear axis? It would likely be: * Faster. * More precise. * More stable. * Much cheaper. So why the humanoid? Because BMW isn’t optimizing this task. They’re training Physical AI. Every successful grasp, every failure, every unexpected situation becomes data that teaches robots to handle the real world. That’s the real investment. The demo is impressive. The business case? I’m not convinced… yet. Are we looking at the future of manufacturing—or the world’s most expensive AI training program? Best regards Ulrich #HumanoidRobots #PhysicalAI #Robotics #Automation #Manufacturing #Engineering #AI #IndustrialAutomation

HUMANOID ROBOTS ARE AMAZING. BUT WHERE IS THE ROI? Watching a humanoid robot work at BMW is genuinely impressive. As an engineer, I love it. But I also can’t ignore one question. For this task, why not use a 6-axis robot on a linear axis? It would likely be: * Faster. * More precise. * More stable. * Much cheaper. So why the humanoid? Because BMW isn’t optimizing this task. They’re training Physical AI. Every successful grasp, every failure, every unexpected situation becomes data that teaches robots to handle the real world. That’s the real investment. The demo is impressive. The business case? I’m not convinced… yet. Are we looking at the future of manufacturing—or the world’s most expensive AI training program? Best regards Ulrich #HumanoidRobots #PhysicalAI #Robotics #Automation #Manufacturing #Engineering #AI #IndustrialAutomation

Advanced humanoid forum

11,496 次观看 • 1 个月前

You asked for it, we delivered. The full keynote is live. In this keynote, we unveil our enhanced vision to become the World’s Ears for AI & Robotics. We are creating the world’s first decentralized infrastructure for real-world audio intelligence. Check-out our new website: Start training Voice AI now & get rewarded: From noise-level mapping to multilingual voice data and the future of live audio streams, Silencio is building the critical audio data layer that will power foundation models, embodied AI, autonomous vehicles, and robotics.

You asked for it, we delivered. The full keynote is live. In this keynote, we unveil our enhanced vision to become the World’s Ears for AI & Robotics. We are creating the world’s first decentralized infrastructure for real-world audio intelligence. Check-out our new website: Start training Voice AI now & get rewarded: From noise-level mapping to multilingual voice data and the future of live audio streams, Silencio is building the critical audio data layer that will power foundation models, embodied AI, autonomous vehicles, and robotics.

Silencio | Voice Data for AI

176,217 次观看 • 11 个月前