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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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134,041 次观看 • 17 天前 •via X (Twitter)

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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

19,189 次观看 • 1 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 Robotics 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 Robotics 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 🦾

14,491 次观看 • 3 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

33,722 次观看 • 3 年前

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

213,365 次观看 • 4 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

382,440 次观看 • 11 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

19,252 次观看 • 2 个月前

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

71,132 次观看 • 6 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

62,040 次观看 • 8 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 • 5 个月前

Video: World’s first humanoid robot labor that swaps its own batteries to work endlessly | Jijo Malayil, Interesting Engineering Walker S2 uses dual-battery balancing and standardized modules to boost efficiency and ensure uninterrupted, optimized performance. In a leap for robotics, China’s UBTech has unveiled the Walker S2, the world’s first humanoid robot capable of fully autonomous battery swapping. Designed for non-stop industrial operations, the Walker S2 can replace its own power pack in just three minutes—no human intervention required. Equipped with advanced anthropomorphic bipedal locomotion and a hot-swappable battery system, Walker S2 is built to operate 24/7 across dynamic industrial environments. According to UBTech, the next-generation humanoid robot marks a major milestone in automation, bringing continuous, hands-free performance to the factory floor. In May 2025, UBTech Robotics and Huawei Technologies inked a significant partnership to accelerate the adoption of humanoid robots across China’s factories and households. Uninterrupted robot operations A video posted by the robotics firm opens with the sleek UBTech Walker S2 humanoid robot working in an industrial setting. The highlight, however, is its autonomous battery swap. Walker S2 approaches the charging station, carefully detaches its depleted power pack, and seamlessly installs a fresh one—all within about three minutes—without any human assistance, according to CGTN. The camera captures close-ups of the robot’s articulated limbs and the intelligent battery-handling mechanism, conveying precision and reliability. As the swap completes, Walker S2 resumes its duties, reinforcing the promise of uninterrupted, 24/7 operations in dynamic factory environments. UBTech’s Walker S2 humanoid robot is equipped with advanced dual-battery power balancing technology and uses standardized battery modules to optimize performance, reports CNEVPOST. This dual-battery system allows the robot to automatically switch to a backup battery in case of a main battery failure, ensuring that critical tasks are carried out without interruption. In addition to battery swapping, the robot can intelligently choose between charging and swapping based on task urgency, allowing it to manage energy dynamically and adapt to real-time operational demands. UBTech highlights these features as a step forward in deploying humanoid robots for industrial and domestic applications, combining flexibility, reliability, and autonomy in one intelligent platform. Factory intelligence upgrade Earlier in the year, UBTech unveiled a major advancement in humanoid robot collaboration, claiming the world’s first deployment of multiple humanoids working together across varied industrial tasks. Demonstrated at Zeekr’s 5G-enabled smart factory, the breakthrough centers on UBTech’s “BrainNet” framework, which orchestrates cooperative behavior through a cloud-device intelligence system. BrainNet integrates a “super brain” for high-level decision-making with an “intelligent sub-brain” for distributed multi-robot control. The super brain, powered by a proprietary large-scale multimodal reasoning model, handles complex production-line scheduling and decision-making. Meanwhile, the sub-brain coordinates real-time tasks using cross-field perception and Transformer-based control for dynamic adaptability. Together, they enable the Walker S1 humanoid robots to move beyond isolated operations and perform coordinated tasks with high precision and speed. The system is built on DeepSeek-R1 reasoning technology and trained on real-world data from automotive factory settings. Leveraging Retrieval-Augmented Generation (RAG), the model adapts to specific job functions and improves scalability across workstations. At Zeekr’s facility, dozens of Walker S1s now collaborate on tasks like assembly, inspection, and part handling. Using semantic VSLAM and shared mapping, they coordinate seamlessly via vision-based navigation and agile manipulation. UBTech says this marks a transition to “Practical Training 2.0,” where humanoid robots operate as a swarm, maximizing efficiency and setting the stage for next-generation intelligent manufacturing.
1:04

Video: World’s first humanoid robot labor that swaps its own batteries to work endlessly | Jijo Malayil, Interesting Engineering Walker S2 uses dual-battery balancing and standardized modules to boost efficiency and ensure uninterrupted, optimized performance. In a leap for robotics, China’s UBTech has unveiled the Walker S2, the world’s first humanoid robot capable of fully autonomous battery swapping. Designed for non-stop industrial operations, the Walker S2 can replace its own power pack in just three minutes—no human intervention required. Equipped with advanced anthropomorphic bipedal locomotion and a hot-swappable battery system, Walker S2 is built to operate 24/7 across dynamic industrial environments. According to UBTech, the next-generation humanoid robot marks a major milestone in automation, bringing continuous, hands-free performance to the factory floor. In May 2025, UBTech Robotics and Huawei Technologies inked a significant partnership to accelerate the adoption of humanoid robots across China’s factories and households. Uninterrupted robot operations A video posted by the robotics firm opens with the sleek UBTech Walker S2 humanoid robot working in an industrial setting. The highlight, however, is its autonomous battery swap. Walker S2 approaches the charging station, carefully detaches its depleted power pack, and seamlessly installs a fresh one—all within about three minutes—without any human assistance, according to CGTN. The camera captures close-ups of the robot’s articulated limbs and the intelligent battery-handling mechanism, conveying precision and reliability. As the swap completes, Walker S2 resumes its duties, reinforcing the promise of uninterrupted, 24/7 operations in dynamic factory environments. UBTech’s Walker S2 humanoid robot is equipped with advanced dual-battery power balancing technology and uses standardized battery modules to optimize performance, reports CNEVPOST. This dual-battery system allows the robot to automatically switch to a backup battery in case of a main battery failure, ensuring that critical tasks are carried out without interruption. In addition to battery swapping, the robot can intelligently choose between charging and swapping based on task urgency, allowing it to manage energy dynamically and adapt to real-time operational demands. UBTech highlights these features as a step forward in deploying humanoid robots for industrial and domestic applications, combining flexibility, reliability, and autonomy in one intelligent platform. Factory intelligence upgrade Earlier in the year, UBTech unveiled a major advancement in humanoid robot collaboration, claiming the world’s first deployment of multiple humanoids working together across varied industrial tasks. Demonstrated at Zeekr’s 5G-enabled smart factory, the breakthrough centers on UBTech’s “BrainNet” framework, which orchestrates cooperative behavior through a cloud-device intelligence system. BrainNet integrates a “super brain” for high-level decision-making with an “intelligent sub-brain” for distributed multi-robot control. The super brain, powered by a proprietary large-scale multimodal reasoning model, handles complex production-line scheduling and decision-making. Meanwhile, the sub-brain coordinates real-time tasks using cross-field perception and Transformer-based control for dynamic adaptability. Together, they enable the Walker S1 humanoid robots to move beyond isolated operations and perform coordinated tasks with high precision and speed. The system is built on DeepSeek-R1 reasoning technology and trained on real-world data from automotive factory settings. Leveraging Retrieval-Augmented Generation (RAG), the model adapts to specific job functions and improves scalability across workstations. At Zeekr’s facility, dozens of Walker S1s now collaborate on tasks like assembly, inspection, and part handling. Using semantic VSLAM and shared mapping, they coordinate seamlessly via vision-based navigation and agile manipulation. UBTech says this marks a transition to “Practical Training 2.0,” where humanoid robots operate as a swarm, maximizing efficiency and setting the stage for next-generation intelligent manufacturing.

Owen Gregorian

35,637 次观看 • 1 年前

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

122,680 次观看 • 4 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 • 4 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 • 9 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

70,372 次观看 • 7 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 • 21 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 • 5 个月前

China’s pretty humanoid robot stuns by opening a car door in a ‘world’s first’ | Jijo Malayil, Interesting Engineering Mornine used onboard sensors and full-body control to locate the handle, adjust posture, and open a car door—no human input needed. AiMOGA Robotics has claimed to have reached a significant milestone in embodied AI with its humanoid robot, Mornine, autonomously opening a car door inside a functioning Chery dealership in China. Relying solely on onboard sensors, full-body motion control, and end-to-end reinforcement learning, Mornine performed the task without any human input. Unlike scripted or teleoperated robots, Mornie identified the door handle, adjusted its posture, and used coordinated force across its limbs and torso to complete the action—demonstrating advanced autonomy in a real-world setting. “The deployment marks one of the first instances of a service robot executing such a high-friction, physical interaction in a live commercial setting,” said the firm in a statement. In April, at the Shanghai Auto Show, automotive brands Omoda and Jaecoo, subsidiaries of Chery Automobile, introduced Mornine, designed for use in car dealerships. From sim to service Opening a car door may seem like a simple task, but AiMOGA Robotics views it as a pivotal moment in robotics—signaling a shift from simulation to real-world service, and from basic command execution to autonomous capability. Using only onboard sensors and full-body motion control, Mornine identified the door handle, adjusted her posture, and applied coordinated force across her limbs to open the door—entirely without human intervention. Mornine’s advanced sensor suite includes 3D LiDAR, depth and wide-angle cameras, and a visual-language model (VLM), enabling real-time perception of door position and opening status. Uniquely, Mornine wasn’t explicitly programmed to recognize door handles. Instead, she learned through reinforcement learning, undergoing millions of simulated cycles to focus on the right region and perform the task independently. “We never explicitly told the robot what a door handle is. It learned to focus on that region by itself,” said the engineering team at AiMOGA Robotics in a statement. The learned model was transferred to the real world using Sim2Real methods. Mornine continuously gathers live sensor data during operation, which feeds into a cloud-based training loop, allowing her to improve through continuous learning in real-world settings, reports Robotics Tomorrow. Now active in multiple Chery 4S dealerships in China, Mornine not only opens car doors but also assists with customer greetings, vehicle introductions, and item delivery—marking a step forward in humanoid robotics for commercial retail environments. AI meets retail Originally introduced as the AiMOGA Robot, Mornine was developed to support dealership sales by performing tasks such as explaining vehicle specifications, leading showroom tours, serving refreshments, and engaging with customers in multiple languages. First conceived by Chery as a virtual character to appeal to Generation Z using metaverse and virtual human technologies, Mornine gradually evolved into a real-world interactive humanoid. After multiple iterations of character and model design, Mornine debuted as a digital persona in animations, livestreams, and promotional content, gaining brand recognition. Chery later expanded the concept beyond the virtual space, resulting in the creation of the AiMOGA humanoid robot. Leveraging Chery’s expertise in autonomous driving, environmental sensing, and control systems, AiMOGA features full-stack capabilities in perception, cognition, decision-making, and execution. It uses multimodal sensing—combining speech, vision, and environmental data—to interpret user gestures, commands, and showroom dynamics. A bionic motion system and automotive-grade hardware enable dexterous movement and upright mobility, while multi-robot collaboration allows for coordinated tasks like guided tours. At the decision-making layer, Deepseek’s large language models enable natural language understanding and personalized interaction. In April 2025, Mornine officially began commercial service as an “Intelligent Sales Consultant” at the OMODA C5 JOYSTAR 4S dealership in Kuala Lumpur, Malaysia—marking her full transition from a virtual concept to a real-world humanoid sales assistant.
0:48

China’s pretty humanoid robot stuns by opening a car door in a ‘world’s first’ | Jijo Malayil, Interesting Engineering Mornine used onboard sensors and full-body control to locate the handle, adjust posture, and open a car door—no human input needed. AiMOGA Robotics has claimed to have reached a significant milestone in embodied AI with its humanoid robot, Mornine, autonomously opening a car door inside a functioning Chery dealership in China. Relying solely on onboard sensors, full-body motion control, and end-to-end reinforcement learning, Mornine performed the task without any human input. Unlike scripted or teleoperated robots, Mornie identified the door handle, adjusted its posture, and used coordinated force across its limbs and torso to complete the action—demonstrating advanced autonomy in a real-world setting. “The deployment marks one of the first instances of a service robot executing such a high-friction, physical interaction in a live commercial setting,” said the firm in a statement. In April, at the Shanghai Auto Show, automotive brands Omoda and Jaecoo, subsidiaries of Chery Automobile, introduced Mornine, designed for use in car dealerships. From sim to service Opening a car door may seem like a simple task, but AiMOGA Robotics views it as a pivotal moment in robotics—signaling a shift from simulation to real-world service, and from basic command execution to autonomous capability. Using only onboard sensors and full-body motion control, Mornine identified the door handle, adjusted her posture, and applied coordinated force across her limbs to open the door—entirely without human intervention. Mornine’s advanced sensor suite includes 3D LiDAR, depth and wide-angle cameras, and a visual-language model (VLM), enabling real-time perception of door position and opening status. Uniquely, Mornine wasn’t explicitly programmed to recognize door handles. Instead, she learned through reinforcement learning, undergoing millions of simulated cycles to focus on the right region and perform the task independently. “We never explicitly told the robot what a door handle is. It learned to focus on that region by itself,” said the engineering team at AiMOGA Robotics in a statement. The learned model was transferred to the real world using Sim2Real methods. Mornine continuously gathers live sensor data during operation, which feeds into a cloud-based training loop, allowing her to improve through continuous learning in real-world settings, reports Robotics Tomorrow. Now active in multiple Chery 4S dealerships in China, Mornine not only opens car doors but also assists with customer greetings, vehicle introductions, and item delivery—marking a step forward in humanoid robotics for commercial retail environments. AI meets retail Originally introduced as the AiMOGA Robot, Mornine was developed to support dealership sales by performing tasks such as explaining vehicle specifications, leading showroom tours, serving refreshments, and engaging with customers in multiple languages. First conceived by Chery as a virtual character to appeal to Generation Z using metaverse and virtual human technologies, Mornine gradually evolved into a real-world interactive humanoid. After multiple iterations of character and model design, Mornine debuted as a digital persona in animations, livestreams, and promotional content, gaining brand recognition. Chery later expanded the concept beyond the virtual space, resulting in the creation of the AiMOGA humanoid robot. Leveraging Chery’s expertise in autonomous driving, environmental sensing, and control systems, AiMOGA features full-stack capabilities in perception, cognition, decision-making, and execution. It uses multimodal sensing—combining speech, vision, and environmental data—to interpret user gestures, commands, and showroom dynamics. A bionic motion system and automotive-grade hardware enable dexterous movement and upright mobility, while multi-robot collaboration allows for coordinated tasks like guided tours. At the decision-making layer, Deepseek’s large language models enable natural language understanding and personalized interaction. In April 2025, Mornine officially began commercial service as an “Intelligent Sales Consultant” at the OMODA C5 JOYSTAR 4S dealership in Kuala Lumpur, Malaysia—marking her full transition from a virtual concept to a real-world humanoid sales assistant.

Owen Gregorian

67,975 次观看 • 11 个月前

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.
0:05

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 次观看 • 4 个月前

I thought this was the most important moment from Elon's interview with Dwarkesh and John Collison Elon is saying the quiet part out loud here. The reality is that without securing a win in the race for AI and robotics, America will fall behind our peers in the future world order Worth a listen "We definitely can't win with just humans because China has 4 times our population. And frankly, America has been winning for so long that we, just like a pro sports team that has been winning for a long time, tend to get complacent and entitled. And that is why they stop winning because they just dont work as hard anymore My observation is that the average work ethic in China is higher than in the U.S. So it's not just that there is 4x the population, the amount of work they put in is higher. We cant win on the human front, and our birth rate has been low for a very long time. U.S. birth rate has been below replacement since roughly 1971. We have more people domestically dying than being born. We definitely cant win on the human front, but we might have a shot at the robot front"
4:16

I thought this was the most important moment from Elon's interview with Dwarkesh and John Collison Elon is saying the quiet part out loud here. The reality is that without securing a win in the race for AI and robotics, America will fall behind our peers in the future world order Worth a listen "We definitely can't win with just humans because China has 4 times our population. And frankly, America has been winning for so long that we, just like a pro sports team that has been winning for a long time, tend to get complacent and entitled. And that is why they stop winning because they just dont work as hard anymore My observation is that the average work ethic in China is higher than in the U.S. So it's not just that there is 4x the population, the amount of work they put in is higher. We cant win on the human front, and our birth rate has been low for a very long time. U.S. birth rate has been below replacement since roughly 1971. We have more people domestically dying than being born. We definitely cant win on the human front, but we might have a shot at the robot front"

Boring_Business

43,199 次观看 • 5 个月前

Introducing Open-TeleVision: with Fully Autonomous policy video👇. We can conduct a long-horizon task with inserting 12 cans nonstop without any interruptions. We offer: 🤖 Highly precise and smooth bimanual manipulation. 📺 Active egocentric vision (with a moving neck) feedback. It is achieved by imitation learning from teleoperation: We propose a VR-based REAL-TIME teleoperation that streams the stereo video observation from the robot camera to the VR device. The robot neck moves as the human head moves, the robot hands move as the human hands move, offering the operator an intuitive experience as the human herself becomes the robot. The devils are all in the details, and how to implement things right: ✅How to perform IK/retargeting for smooth and precise control. ✅How to do all these and also stream stereo video without no latency, all in real time. We released our code here: Active head hardware design: 1/n
0:33

Introducing Open-TeleVision: with Fully Autonomous policy video👇. We can conduct a long-horizon task with inserting 12 cans nonstop without any interruptions. We offer: 🤖 Highly precise and smooth bimanual manipulation. 📺 Active egocentric vision (with a moving neck) feedback. It is achieved by imitation learning from teleoperation: We propose a VR-based REAL-TIME teleoperation that streams the stereo video observation from the robot camera to the VR device. The robot neck moves as the human head moves, the robot hands move as the human hands move, offering the operator an intuitive experience as the human herself becomes the robot. The devils are all in the details, and how to implement things right: ✅How to perform IK/retargeting for smooth and precise control. ✅How to do all these and also stream stereo video without no latency, all in real time. We released our code here: Active head hardware design: 1/n

Xiaolong Wang

25,572 次观看 • 2 年前