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"A parcel with snacks has been delivered for Flexion. Retrieve it using the stairs and come up using the elevator. Then unpack it and place the items into the empty drawer on the shelf in the snack area." One instruction. No human operator. Everything that follows is autonomous. Today...

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Flexion

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140,467 次观看 • 1 个月前 •via X (Twitter)

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There’s not enough hype about Flexion Robotics. They are literally one of the sexiest European companies these days! 🇪🇺 I had a chance to meet the founder Nikita last year and learn his story. He turned down offers from top physical AI companies to build his own. The reason was his specific vision of how robot intelligence should be built to actually scale. So he decided to build one himself. Flexion is building an ultimate brain for humanoid robots. Yesterday they released Reflect v1.0. It’s an intelligence platform for long-horizon autonomous missions. The system has three layers. At the top, a custom vision-language model acts as mission control, observing the robot's camera feed and replanning continuously. Below that, a motion layer combines a VLA trained on real-world data with reinforcement learning-based skills. It translates perception into navigation, scene interaction, and manipulation commands. At the bottom, a whole-body controller runs in real-time, maintaining balance while the robot navigates and performs tasks. I highly recommend them visiting Zurich! 🇨🇭 Nikita Rudin cheapeu bas on the launch! 🎩 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://
0:23

There’s not enough hype about Flexion Robotics. They are literally one of the sexiest European companies these days! 🇪🇺 I had a chance to meet the founder Nikita last year and learn his story. He turned down offers from top physical AI companies to build his own. The reason was his specific vision of how robot intelligence should be built to actually scale. So he decided to build one himself. Flexion is building an ultimate brain for humanoid robots. Yesterday they released Reflect v1.0. It’s an intelligence platform for long-horizon autonomous missions. The system has three layers. At the top, a custom vision-language model acts as mission control, observing the robot's camera feed and replanning continuously. Below that, a motion layer combines a VLA trained on real-world data with reinforcement learning-based skills. It translates perception into navigation, scene interaction, and manipulation commands. At the bottom, a whole-body controller runs in real-time, maintaining balance while the robot navigates and performs tasks. I highly recommend them visiting Zurich! 🇨🇭 Nikita Rudin cheapeu bas on the launch! 🎩 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://

Lukas Ziegler

23,161 次观看 • 1 个月前

We ran 300 fully autonomous live demonstrations over 3 days at ICRA 2026. The task: a humanoid navigating stairs, picking up a box from the floor and placing it on a table. Simple to describe, but hard to execute reliably when your robot is making every decision on its own at a conference with new surroundings and a crowd watching live. This is just a glimpse. We've been pushing our stack much further and we'll be sharing more very soon. More information in the thread. #HumanoidRobots #ICRA2026 #Flexion
0:43

We ran 300 fully autonomous live demonstrations over 3 days at ICRA 2026. The task: a humanoid navigating stairs, picking up a box from the floor and placing it on a table. Simple to describe, but hard to execute reliably when your robot is making every decision on its own at a conference with new surroundings and a crowd watching live. This is just a glimpse. We've been pushing our stack much further and we'll be sharing more very soon. More information in the thread. #HumanoidRobots #ICRA2026 #Flexion

Flexion

19,634 次观看 • 2 个月前

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏
18:51

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏

Andreas Klinger 🦾

14,761 次观看 • 4 个月前

AI + robotics research is starting to pick up steam. We can now instruct Spot using natural language using Language-guided Skill Coordination (LSC). A user provides a natural language instruction: "Bring me the chocolates box, cereal box, and pill bottle, and put them on the bedroom table" and the robot navigates to the location of the target objects and places them on the room table. Current versions of Spot rely on a fixed-set vocabulary that cannot generalize to diverse instructions. In this demo, the researchers presented a method that uses large language models to receive a free-form natural language instruction for object rearrangement, which it then executes. It combines: -A voice-to-text model that processes the instructions into text -An LLM that converts natural language instruction to call a library of skills -A perception module that provides ground truth locations and visual object detection And what your left with is a glimpse into what our future will hold: robots executing tasks from human voice prompts.
0:30

AI + robotics research is starting to pick up steam. We can now instruct Spot using natural language using Language-guided Skill Coordination (LSC). A user provides a natural language instruction: "Bring me the chocolates box, cereal box, and pill bottle, and put them on the bedroom table" and the robot navigates to the location of the target objects and places them on the room table. Current versions of Spot rely on a fixed-set vocabulary that cannot generalize to diverse instructions. In this demo, the researchers presented a method that uses large language models to receive a free-form natural language instruction for object rearrangement, which it then executes. It combines: -A voice-to-text model that processes the instructions into text -An LLM that converts natural language instruction to call a library of skills -A perception module that provides ground truth locations and visual object detection And what your left with is a glimpse into what our future will hold: robots executing tasks from human voice prompts.

AI Breakfast

33,725 次观看 • 3 年前

Gemini Robotics ER 2 is our most capable embodied reasoning model designed for physical AI 🤖 Built as a high-level brain for robotics, the model connects directly to the Gemini Live API. It processes continuous video streams to track progress, call tools, search the web, and command an action model in real time. Upgrades for developers include: + Enhanced progress tracking + Real-time mid-execution failure detection + Multi-robot collaboration + Advanced safety instruction following
2:31

Gemini Robotics ER 2 is our most capable embodied reasoning model designed for physical AI 🤖 Built as a high-level brain for robotics, the model connects directly to the Gemini Live API. It processes continuous video streams to track progress, call tools, search the web, and command an action model in real time. Upgrades for developers include: + Enhanced progress tracking + Real-time mid-execution failure detection + Multi-robot collaboration + Advanced safety instruction following

Google AI Developers

20,880 次观看 • 17 天前

Today I am extremely proud to introduce the Fury Autonomous Vehicle Orchestrator 🇺🇸 For the past 12 months, the Scout AI team has been building this quietly. Now we’re finally ready to unveil it to the world Just like AI agents are transforming the digital world, Fury Orchestrator brings that same agentic intelligence into the physical world for the U.S. warfighter It is the first agentic system that can take a commander’s high-level mission intent, spoken or typed in natural language, and turn it into coordinated autonomous action across a mixed fleet of unmanned assets More in 🧵
3:15

Today I am extremely proud to introduce the Fury Autonomous Vehicle Orchestrator 🇺🇸 For the past 12 months, the Scout AI team has been building this quietly. Now we’re finally ready to unveil it to the world Just like AI agents are transforming the digital world, Fury Orchestrator brings that same agentic intelligence into the physical world for the U.S. warfighter It is the first agentic system that can take a commander’s high-level mission intent, spoken or typed in natural language, and turn it into coordinated autonomous action across a mixed fleet of unmanned assets More in 🧵

Colby Adcock

213,420 次观看 • 5 个月前

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.
2:48

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

383,431 次观看 • 1 年前

Chinese humanoid robotics company LimX Dynamics has unveiled COSA (Cognitive Operating System of Agents). COSA is described as a unified "brain-body" architecture that allows the robot to think and act simultaneously in the real world. It integrates: - high-level cognition (reasoning, planning, adaptation) - and whole-body motion control (low-latency dynamic locomotion, manipulation) It enables the Oli robot to act on natural language instructions and perform tasks that involve both walking and manipulation while being adaptive to interruptions.
1:18

Chinese humanoid robotics company LimX Dynamics has unveiled COSA (Cognitive Operating System of Agents). COSA is described as a unified "brain-body" architecture that allows the robot to think and act simultaneously in the real world. It integrates: - high-level cognition (reasoning, planning, adaptation) - and whole-body motion control (low-latency dynamic locomotion, manipulation) It enables the Oli robot to act on natural language instructions and perform tasks that involve both walking and manipulation while being adaptive to interruptions.

The Humanoid Hub

71,168 次观看 • 7 个月前

Happy to share what I’ve been working on since joining Genesis! GENE-26.5 is a one-of-a-kind, robotics-native multimodal foundation model that learns from diverse, in-the-wild data across modalities and outputs actions enabling a 54-DoF robot system to perform the most dexterous, long-horizon manipulation tasks to date—approaching human-level capability. This is the result of innovations across the full stack—data collection and processing, robot systems, model architecture, training strategies, and scalable evaluation infrastructure.
2:23

Happy to share what I’ve been working on since joining Genesis! GENE-26.5 is a one-of-a-kind, robotics-native multimodal foundation model that learns from diverse, in-the-wild data across modalities and outputs actions enabling a 54-DoF robot system to perform the most dexterous, long-horizon manipulation tasks to date—approaching human-level capability. This is the result of innovations across the full stack—data collection and processing, robot systems, model architecture, training strategies, and scalable evaluation infrastructure.

Zu Wang

19,673 次观看 • 3 个月前

How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥
1:32

How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥

Zhengyi “Zen” Luo

62,326 次观看 • 9 个月前

Foundation models are enough to solve robotics! Unfortunately, this is not true. We keep hearing that Vision-Language-Action (VLA) models struggle because of the gap between static training and the dynamic real world. A German startup (Sereact) just released a solution that bridges this gap perfectly. They are introducing a new paradigm called Interactive RL Policy Patching. It's a distributed framework that allows robots to self-learn from human corrections without needing full retraining. When a robot fails, a human operator provides a brief "patch" or demonstration. The system then uses online off-policy reinforcement learning to update the behavior instantly. This is powered by a massive foundation model trained on hundreds of millions of interactions from over 100 deployed robot stations. The best part is the distributed parameter synchronization... When one robot learns a fix, the update is published fleet-wide... meaning the entire swarm gets smarter from a single human intervention. They are already proving this on complex manipulation tasks like shoe unboxing and screw sorting, drastically reducing the data needed to handle edge cases. Real-world environments are unforgiving, and I love seeing systems that can actually adapt on the fly! 📍 More info:
0:38

Foundation models are enough to solve robotics! Unfortunately, this is not true. We keep hearing that Vision-Language-Action (VLA) models struggle because of the gap between static training and the dynamic real world. A German startup (Sereact) just released a solution that bridges this gap perfectly. They are introducing a new paradigm called Interactive RL Policy Patching. It's a distributed framework that allows robots to self-learn from human corrections without needing full retraining. When a robot fails, a human operator provides a brief "patch" or demonstration. The system then uses online off-policy reinforcement learning to update the behavior instantly. This is powered by a massive foundation model trained on hundreds of millions of interactions from over 100 deployed robot stations. The best part is the distributed parameter synchronization... When one robot learns a fix, the update is published fleet-wide... meaning the entire swarm gets smarter from a single human intervention. They are already proving this on complex manipulation tasks like shoe unboxing and screw sorting, drastically reducing the data needed to handle edge cases. Real-world environments are unforgiving, and I love seeing systems that can actually adapt on the fly! 📍 More info:

Ilir Aliu

18,210 次观看 • 6 个月前

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.
3:08

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.

Dustin

122,769 次观看 • 5 个月前

Jensen Huang just validated Elon Musk’s entire ecosystem in a single breath. Not one product. All of it. Huang: “The work he’s doing in Grok, self-driving cars, and Optimus. These are all world-class. Every single one of them is revolutionary. Every single one of them is going to be a gigantic opportunity.” To the public, a chatbot, a car, and a robot look like three separate bets. They are one project. The total automation of human cognition and physical labor. A digital brain. A spatial nervous system. A physical body. Musk is building all three simultaneously. Huang is supplying the compute to fuse them. Huang: “We do a lot of business with Tesla and xAI. Elon is an extraordinary engineer, and I love working with him. We’ve built some amazing computers together, and we’re going to build many more.” This is not a vendor relationship. It is the most consequential technological alliance in history and most people think it is a business partnership. Then Huang said what should end every debate about Optimus. Huang: “This is the first robot that really has a chance to achieve the high volume and technology scale necessary to advance technology.” Huang: “Right around the corner. Likely to be the next multi-trillion dollar industry.” The humanoid robot race will not be won in a research lab. It will be won on the manufacturing floor. Every other robotics company on earth can build a robot. Tesla can flood the planet with them. Because Tesla already knows how to stamp metal, build batteries, and deploy autonomous inference at global scale. The rest of the industry has prototypes. Tesla has the most sophisticated manufacturing operation on earth. When the world’s leading chipmaker calls your robot the next multi-trillion dollar industry, the debate is over. One supplies the chips. One builds everything the chips make possible. When that infrastructure scales across Grok, FSD, and Optimus simultaneously, the question stops being whether this changes everything. It becomes how fast.
1:08

Jensen Huang just validated Elon Musk’s entire ecosystem in a single breath. Not one product. All of it. Huang: “The work he’s doing in Grok, self-driving cars, and Optimus. These are all world-class. Every single one of them is revolutionary. Every single one of them is going to be a gigantic opportunity.” To the public, a chatbot, a car, and a robot look like three separate bets. They are one project. The total automation of human cognition and physical labor. A digital brain. A spatial nervous system. A physical body. Musk is building all three simultaneously. Huang is supplying the compute to fuse them. Huang: “We do a lot of business with Tesla and xAI. Elon is an extraordinary engineer, and I love working with him. We’ve built some amazing computers together, and we’re going to build many more.” This is not a vendor relationship. It is the most consequential technological alliance in history and most people think it is a business partnership. Then Huang said what should end every debate about Optimus. Huang: “This is the first robot that really has a chance to achieve the high volume and technology scale necessary to advance technology.” Huang: “Right around the corner. Likely to be the next multi-trillion dollar industry.” The humanoid robot race will not be won in a research lab. It will be won on the manufacturing floor. Every other robotics company on earth can build a robot. Tesla can flood the planet with them. Because Tesla already knows how to stamp metal, build batteries, and deploy autonomous inference at global scale. The rest of the industry has prototypes. Tesla has the most sophisticated manufacturing operation on earth. When the world’s leading chipmaker calls your robot the next multi-trillion dollar industry, the debate is over. One supplies the chips. One builds everything the chips make possible. When that infrastructure scales across Grok, FSD, and Optimus simultaneously, the question stops being whether this changes everything. It becomes how fast.

Dustin

66,374 次观看 • 5 个月前

We’ve raised $50 million in Series A funding from DST Global Partners, NVIDIA (NVIDIA’s venture capital arm), redalpine , prosusventures , and Moonfire Ventures , following our $7.35M seed round from Frst , Moonfire Ventures , and redalpine just a few months earlier, to build the autonomy stack that makes humanoid robots adaptive, intelligent, and ready for real-world deployment at scale. In less than a year, our team has shown that long-horizon whole-body humanoid control can scale across hardware and tasks by leveraging the power of simulation and reinforcement learning. This funding will help us grow our team, scale our compute and robot fleets, and accelerate the commercialization of our autonomy stack with OEM partners globally. You can find more details in the links shared in the comments.
1:47

We’ve raised $50 million in Series A funding from DST Global Partners, NVIDIA (NVIDIA’s venture capital arm), redalpine , prosusventures , and Moonfire Ventures , following our $7.35M seed round from Frst , Moonfire Ventures , and redalpine just a few months earlier, to build the autonomy stack that makes humanoid robots adaptive, intelligent, and ready for real-world deployment at scale. In less than a year, our team has shown that long-horizon whole-body humanoid control can scale across hardware and tasks by leveraging the power of simulation and reinforcement learning. This funding will help us grow our team, scale our compute and robot fleets, and accelerate the commercialization of our autonomy stack with OEM partners globally. You can find more details in the links shared in the comments.

Flexion

71,334 次观看 • 8 个月前

Tesla's Chief Designer Franz on Optimus: "It's going to be huge piece of Tesla, a huge piece in our world, as we look into the future, the ability to take menial tasks and things that you don't necessarily want to do and have them uploaded to somebody, a human, that can do that. We've been designing the robot that really looks at how humans work in the world. This world is designed around humans, and so I think having a robot that can do the things that humans can do can really start to take those menial tasks away and enrich in our lives, make our lives better we get to spend more time doing the things that we love. It's been incredibly fast paced, but the progress is, like, unbelievable in the short amount of time that the team has been working on it, and we've been growing the team and building the project the same time. I think we're breaking ground in this space everyday. The team is really energized and fired out. Elon Musk is incredibly involved in this. It's a great project to be a part of, and seeing where the future is gonna go and how it's going to enrich our lives is super exciting."
1:13

Tesla's Chief Designer Franz on Optimus: "It's going to be huge piece of Tesla, a huge piece in our world, as we look into the future, the ability to take menial tasks and things that you don't necessarily want to do and have them uploaded to somebody, a human, that can do that. We've been designing the robot that really looks at how humans work in the world. This world is designed around humans, and so I think having a robot that can do the things that humans can do can really start to take those menial tasks away and enrich in our lives, make our lives better we get to spend more time doing the things that we love. It's been incredibly fast paced, but the progress is, like, unbelievable in the short amount of time that the team has been working on it, and we've been growing the team and building the project the same time. I think we're breaking ground in this space everyday. The team is really energized and fired out. Elon Musk is incredibly involved in this. It's a great project to be a part of, and seeing where the future is gonna go and how it's going to enrich our lives is super exciting."

DogeDesigner

202,945 次观看 • 10 个月前

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
2:32

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 次观看 • 1 个月前

ELON MUSK: "Optimus will be the biggest product ever, but it is a very complex problem to solve. It's one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that, if you simply ask it to do something or show it a video, it can do the task without any programming. No one's ever achieved this, and there are many challenges in the design of the robot to achieve sufficient dexterity and to be very reliable and have long wear and tear, meaning it needs to be out in the field and not break down. Otherwise, you've got sort of a 70 kilogram robot that just flopped over and you've got to carry it out like a body, you know, it doesn't have wheels. And we don't want Optimus to go haywire. So it's a lot of work to scale, to get the design right and to scale production. This is going to be the hardest product to scale manufacturing that we've ever made at Tesla because everything on the robot is new. And the difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot. With Optimus, there is no supply chain. So we've had to build up a supply chain in its entirety or in-house the production. The Optimus production line that we're building out in Fremont in place of what used to be the Model S & X production, it looks incredible. It's quite stunning to see."
2:42

ELON MUSK: "Optimus will be the biggest product ever, but it is a very complex problem to solve. It's one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that, if you simply ask it to do something or show it a video, it can do the task without any programming. No one's ever achieved this, and there are many challenges in the design of the robot to achieve sufficient dexterity and to be very reliable and have long wear and tear, meaning it needs to be out in the field and not break down. Otherwise, you've got sort of a 70 kilogram robot that just flopped over and you've got to carry it out like a body, you know, it doesn't have wheels. And we don't want Optimus to go haywire. So it's a lot of work to scale, to get the design right and to scale production. This is going to be the hardest product to scale manufacturing that we've ever made at Tesla because everything on the robot is new. And the difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot. With Optimus, there is no supply chain. So we've had to build up a supply chain in its entirety or in-house the production. The Optimus production line that we're building out in Fremont in place of what used to be the Model S & X production, it looks incredible. It's quite stunning to see."

DogeDesigner

307,708 次观看 • 24 天前

⚡SHARPA LEAVES THE LAB, HEADS TO DAIRY QUEEN Sharpa is taking its 22-DoF Wave hand beyond demos with Project Edison, preparing its North robot for a real Dairy Queen deployment in Shanghai. Humanoid Scott got an early look at the project. A few things that really stood out from our conversation: > Sharpa is building the full stack around the hand, including the robot arms, mobile base and AI > North has to work in the existing kitchen, with no redesign of the workspace around the robot > Scott Walter says making a Blizzard involves roughly 50–60 steps through an existing human workflow > The preview footage is teleoperated and sped up. Sharpa expects the released system to be fully autonomous But the bigger story here is Sharpa taking dexterous robot hands from impressive demos to complete systems capable of useful commercial work. And Sharpa founder David Li has offered to join us on Over The Horizon to take us deeper inside Project Edison. Great discussion with Humanoid Scott and Mehrdad Farimani. Looking forward to hopefully having Sharpa and David Li join us for a deeper dive into their work! ⚡Full episode linked below in the thread!👇🏽
3:28

⚡SHARPA LEAVES THE LAB, HEADS TO DAIRY QUEEN Sharpa is taking its 22-DoF Wave hand beyond demos with Project Edison, preparing its North robot for a real Dairy Queen deployment in Shanghai. Humanoid Scott got an early look at the project. A few things that really stood out from our conversation: > Sharpa is building the full stack around the hand, including the robot arms, mobile base and AI > North has to work in the existing kitchen, with no redesign of the workspace around the robot > Scott Walter says making a Blizzard involves roughly 50–60 steps through an existing human workflow > The preview footage is teleoperated and sped up. Sharpa expects the released system to be fully autonomous But the bigger story here is Sharpa taking dexterous robot hands from impressive demos to complete systems capable of useful commercial work. And Sharpa founder David Li has offered to join us on Over The Horizon to take us deeper inside Project Edison. Great discussion with Humanoid Scott and Mehrdad Farimani. Looking forward to hopefully having Sharpa and David Li join us for a deeper dive into their work! ⚡Full episode linked below in the thread!👇🏽

Royden D'Souza

369,693 次观看 • 23 天前

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:56

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,898 次观看 • 6 个月前