Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

New Unsupervised Learning with Karol Hausman & Danny Driess (Physical Intelligence) on building generalist robotics foundation models and: - What’s next in AI x robotics - Biggest outstanding questions - How they 10x’d model training speed - Open sourcing π 0 - Breakthroughs in generalization Spotify: Apple: YouTube:

Jacob Effron

3,793 subscribers

13,961 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

We are launching a new Open Physical AI Lab to solve the generalization in robotics. Discussion with Caroline Hyde and Ed Ludlow on Bloomberg Tech this morning -

We are launching a new Open Physical AI Lab to solve the generalization in robotics. Discussion with Caroline Hyde and Ed Ludlow on Bloomberg Tech this morning -

amit

30,318 views • 4 months ago

Alphabet (Google's parent company) acquired a stake in Physical Intelligence. San Francisco-based Physical Intelligence, an AI and robotics startup, has secured $600 million in fresh funding, pushing its post-money valuation to $5.6 billion. - The round was led by Alphabet's independent growth fund CapitalG. - Existing backers who participated in this round include Lux Capital, Thrive Capital, and Jeff Bezos. - New participants include Index Ventures and T. Rowe Price, among others. Physical Intelligence is building general-purpose AI foundation models and learning algorithms designed to act as the "brain" for robots. Here's their latest π*0.6 model in actio

Alphabet (Google's parent company) acquired a stake in Physical Intelligence. San Francisco-based Physical Intelligence, an AI and robotics startup, has secured $600 million in fresh funding, pushing its post-money valuation to $5.6 billion. - The round was led by Alphabet's independent growth fund CapitalG. - Existing backers who participated in this round include Lux Capital, Thrive Capital, and Jeff Bezos. - New participants include Index Ventures and T. Rowe Price, among others. Physical Intelligence is building general-purpose AI foundation models and learning algorithms designed to act as the "brain" for robots. Here's their latest π*0.6 model in actio

The Humanoid Hub

244,972 views • 10 months ago

Chelsea Finn (Chelsea Finn) on building general-purpose robotics, and bringing intelligence into the physical world. At AI Startup School in San Francisco. 00:00 - General Purpose Robots 00:11 - Challenges in Robotics Applications 00:57 - Physical Intelligence: A New Approach 01:47 - Learning from Language Models 02:08 - Data Sources for Training Robots 03:32 - Training with Real-World Data 04:39 - Initial Successes and Challenges 09:10 - Breakthrough in Robot Training 11:03 - Improving Performance 15:43 - Expanding Capabilities 17:34 - Robots in Unseen Environments 25:54 - Handling Open-Ended Prompts 29:36 - Evaluating Robot Performance 30:03 - Future Directions and Challenges 31:27 - Audience Q&A

Chelsea Finn (Chelsea Finn) on building general-purpose robotics, and bringing intelligence into the physical world. At AI Startup School in San Francisco. 00:00 - General Purpose Robots 00:11 - Challenges in Robotics Applications 00:57 - Physical Intelligence: A New Approach 01:47 - Learning from Language Models 02:08 - Data Sources for Training Robots 03:32 - Training with Real-World Data 04:39 - Initial Successes and Challenges 09:10 - Breakthrough in Robot Training 11:03 - Improving Performance 15:43 - Expanding Capabilities 17:34 - Robots in Unseen Environments 25:54 - Handling Open-Ended Prompts 29:36 - Evaluating Robot Performance 30:03 - Future Directions and Challenges 31:27 - Audience Q&A

Y Combinator

129,834 views • 1 year ago

Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗 #Robotics #EmbodiedAI #FoundationModels #RobotLearning #XiaomiAI #XiaomiRobotics

Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗 #Robotics #EmbodiedAI #FoundationModels #RobotLearning #XiaomiAI #XiaomiRobotics

Xiaomi Tech

23,828 views • 2 months ago

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 views • 6 months ago

OpenRoboto miners pushed Physical Intelligence’s π0.5 model from ~55% to nearly 90% success on simulated robot tasks in under a month. On the latest Novelty Search, OpenRoboto explains how Bittensor’s Subnet 80 develops open robotics models through competition and tests them on real hardware. With Shift, OpenRoboto is building a decentralized network for robotics training data, rewarding contributors who capture real workplace skills using OR-S1 devices introduced in partnership with General Intelligence Labs. Hosted by const Full episode in the first comment

OpenRoboto miners pushed Physical Intelligence’s π0.5 model from ~55% to nearly 90% success on simulated robot tasks in under a month. On the latest Novelty Search, OpenRoboto explains how Bittensor’s Subnet 80 develops open robotics models through competition and tests them on real hardware. With Shift, OpenRoboto is building a decentralized network for robotics training data, rewarding contributors who capture real workplace skills using OR-S1 devices introduced in partnership with General Intelligence Labs. Hosted by const Full episode in the first comment

Openτensor Foundaτion

19,996 views • 5 days ago

Physical AI is here—the next wave of AI and the foundation of the next industrial revolution. NVIDIA founder and CEO Jensen Huang outlines the full stack making it possible: Cosmos and Omniverse to develop physical AI in virtual worlds, Isaac and Newton where robots learn skills in physics-simulated gyms, and Jetson robotics computers where their intelligence runs. Together with Japan’s robotics and manufacturing leaders, NVIDIA is creating a new era of industrial automation.

Physical AI is here—the next wave of AI and the foundation of the next industrial revolution. NVIDIA founder and CEO Jensen Huang outlines the full stack making it possible: Cosmos and Omniverse to develop physical AI in virtual worlds, Isaac and Newton where robots learn skills in physics-simulated gyms, and Jetson robotics computers where their intelligence runs. Together with Japan’s robotics and manufacturing leaders, NVIDIA is creating a new era of industrial automation.

NVIDIA

134,836 views • 2 months ago

New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact

New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact

The Humanoid Hub

71,481 views • 11 months ago

EPISODE 161: The Robotics GPT-3 Moment Joe Lonsdale sits down with Pete Florence Pete and Generalist are changing everything we know about robotics: -- robots trained on new tasks in seconds (seconds!) -- self-taught ambidexterity & other breakthroughs -- 10X more science and how the world will change (00:00) Episode intro (01:30) Bay Area to MIT and DeepMind (03:55) Pete's epiphany: robots sitting still (08:30) Why intelligence, not hardware, is the next frontier (13:20) The GPT-3 era of robotics (14:55) Why leave DeepMind? (16:50) How to train robot brains (18:30) Emergent ambidexterity (20:40) LLMs vs robot world models (23:50) GEN-0, GEN-1 & scaling laws in robotics (27:28) 10x more science per year (32:50) Skilled trades, reindustrialization & future of robots

EPISODE 161: The Robotics GPT-3 Moment Joe Lonsdale sits down with Pete Florence Pete and Generalist are changing everything we know about robotics: -- robots trained on new tasks in seconds (seconds!) -- self-taught ambidexterity & other breakthroughs -- 10X more science and how the world will change (00:00) Episode intro (01:30) Bay Area to MIT and DeepMind (03:55) Pete's epiphany: robots sitting still (08:30) Why intelligence, not hardware, is the next frontier (13:20) The GPT-3 era of robotics (14:55) Why leave DeepMind? (16:50) How to train robot brains (18:30) Emergent ambidexterity (20:40) LLMs vs robot world models (23:50) GEN-0, GEN-1 & scaling laws in robotics (27:28) 10x more science per year (32:50) Skilled trades, reindustrialization & future of robots

American Optimist

346,265 views • 1 month ago

$10T is pouring into AI — but intelligence can’t stay trapped on screens. The next leap must be into the physical world. John Hanke, CEO of Niantic Spatial, writes in The Information about the company is building a Large Geospatial Model so AI can understand, navigate, and act in the real world. 🔗Read more: #Paywall #NianticSpatial #JohnHanke #AI #Robotics #GeospatialAI #LLMtoLGM

$10T is pouring into AI — but intelligence can’t stay trapped on screens. The next leap must be into the physical world. John Hanke, CEO of Niantic Spatial, writes in The Information about the company is building a Large Geospatial Model so AI can understand, navigate, and act in the real world. 🔗Read more: #Paywall #NianticSpatial #JohnHanke #AI #Robotics #GeospatialAI #LLMtoLGM

Niantic Spatial 🌎

26,143 views • 7 months ago

Sergey Levine (Sergey Levine) is one of the world's top robotics researchers and co-founder of Physical Intelligence. We talked about where humanoid robotics is today, thoughts on the Chinese robotics ecosystem, and his predictions for future timelines. In this episode: • Current state of robotics and surprising capabilities so far • Chinese robotics compared to US ecosystem • If OpenAI and Anthropic got into robotics • His top robotics research paper recommendation • Predictions for when humanoid robotics will land Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:37 Where are we today 04:20 Most surprising capabilities so far 07:03 The most inspiring real world robotics 08:36 If OpenAI or Anthropic got into robotics 10:22 Chinese robotics 13:15 Will one lab breakout from the rest 16:59 Thoughts on a concrete roadmap 21:03 Generalization and demonstrating it 26:04 Types of data and which is best for robotics 34:34 Why humanoid robotics differs from Waymo 37:10 If humanoid robotics failed here is why 39:55 Are there hot take modeling architectures in robotics 42:05 Thoughts on AI safety in robotics 46:44 Top robotics research paper recommendation 49:35 Why is Boston Dynamics less top of mind 53:47 Advice for his younger self 56:42 Outro

Sergey Levine (Sergey Levine) is one of the world's top robotics researchers and co-founder of Physical Intelligence. We talked about where humanoid robotics is today, thoughts on the Chinese robotics ecosystem, and his predictions for future timelines. In this episode: • Current state of robotics and surprising capabilities so far • Chinese robotics compared to US ecosystem • If OpenAI and Anthropic got into robotics • His top robotics research paper recommendation • Predictions for when humanoid robotics will land Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:37 Where are we today 04:20 Most surprising capabilities so far 07:03 The most inspiring real world robotics 08:36 If OpenAI or Anthropic got into robotics 10:22 Chinese robotics 13:15 Will one lab breakout from the rest 16:59 Thoughts on a concrete roadmap 21:03 Generalization and demonstrating it 26:04 Types of data and which is best for robotics 34:34 Why humanoid robotics differs from Waymo 37:10 If humanoid robotics failed here is why 39:55 Are there hot take modeling architectures in robotics 42:05 Thoughts on AI safety in robotics 46:44 Top robotics research paper recommendation 49:35 Why is Boston Dynamics less top of mind 53:47 Advice for his younger self 56:42 Outro

Ryan Peterman

72,513 views • 1 month ago

Excitement about world-action models and robot learning has never been higher — they promise a way to use human egocentric data to train massive robotics models which can provide the “GPT” moment for robotics and unlock general-purpose embodied intelligence. And yet there’s been little concrete demonstration of scaling in robot learning. Dyna Robotics aims to change that, with an in-depth look at how scaling works as they approach 1 million hours of training data. Jason Ma Tianyu Li Anh-Quan Pham and Chet Bhateja joined us to tell us more. They show that as the amount of data they use in pretraining increased, they saw predictable, statistically significant gains on accuracy metrics on held-out data (data not seen during training). They go on to talk about what they learned, and show how this can be applied to many different problems. Watch Episode #99 of RoboPapers, with Michael Cho - Rbt/Acc Chris Paxton Jiafei Duan today to learn more!

Excitement about world-action models and robot learning has never been higher — they promise a way to use human egocentric data to train massive robotics models which can provide the “GPT” moment for robotics and unlock general-purpose embodied intelligence. And yet there’s been little concrete demonstration of scaling in robot learning. Dyna Robotics aims to change that, with an in-depth look at how scaling works as they approach 1 million hours of training data. Jason Ma Tianyu Li Anh-Quan Pham and Chet Bhateja joined us to tell us more. They show that as the amount of data they use in pretraining increased, they saw predictable, statistically significant gains on accuracy metrics on held-out data (data not seen during training). They go on to talk about what they learned, and show how this can be applied to many different problems. Watch Episode #99 of RoboPapers, with Michael Cho - Rbt/Acc Chris Paxton Jiafei Duan today to learn more!

RoboPapers

26,197 views • 1 month ago

Been looking into RICE AI ( ◉ - ◉ ) lately, and it’s one of the few projects actually tackling a real challenge in robotics and AGI: the lack of high-quality training data. Robots can’t get truly intelligent without it. They building a decentralized way to collect and share robotics data, using a DePIN network to train physical AI foundation models. The project was even hand-picked by CZ as a winner of the Dubai BNB Chain Demo Day, and it’s backed by Floki while also being part of the NVIDIA Inception Program. They also launched FLOKI Minibot M1 - a small AI companion that doubles as a data node. Every interaction with it feeds the RICE AI Foundry, the first decentralized platform for Robotics Foundation Models, and contributors earn $RICE tokens in return. And it’s working: – 800 minibots sold in 24h – 33,000% oversubscribed Binance IDO – 500+ robots already deployed worldwide Feels like a project with real long-term potential. Worth keeping an eye on. RICE AI ( ◉ - ◉ )

Been looking into RICE AI ( ◉ - ◉ ) lately, and it’s one of the few projects actually tackling a real challenge in robotics and AGI: the lack of high-quality training data. Robots can’t get truly intelligent without it. They building a decentralized way to collect and share robotics data, using a DePIN network to train physical AI foundation models. The project was even hand-picked by CZ as a winner of the Dubai BNB Chain Demo Day, and it’s backed by Floki while also being part of the NVIDIA Inception Program. They also launched FLOKI Minibot M1 - a small AI companion that doubles as a data node. Every interaction with it feeds the RICE AI Foundry, the first decentralized platform for Robotics Foundation Models, and contributors earn $RICE tokens in return. And it’s working: – 800 minibots sold in 24h – 33,000% oversubscribed Binance IDO – 500+ robots already deployed worldwide Feels like a project with real long-term potential. Worth keeping an eye on. RICE AI ( ◉ - ◉ )

Christiaan

12,696 views • 1 year ago

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

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

Silencio | Voice Data for AI

176,480 views • 1 year ago

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

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

Pulkit Agrawal

346,218 views • 5 months ago

I sat down with Lukasz Kaiser to get into whether the architecture he helped invent is actually enough, and what's next in generalization, coding agents, RL and more. Lukasz co-authored "Attention Is All You Need," the paper that introduced the transformer and worked on reasoning models at OpenAI so he’s been a key part of major shifts in the field. We hit on: ▪️ The case for and against a new architecture coming after the transformer ▪️ What’s required for model generalization in the physical world ▪️ How much coding agents have improved his AI research productivity ▪️ The next domains for RL ▪️ Why Anthropic initially won coding ▪️ Future research directions he’s excited about 0:00 Intro 1:12 Transformers vs. Human Learning 8:37 How Do We Get Physical World Generalization? 10:52 What Comes After Transformers 13:59 How Much Have Agents Improved Lukasz's AI Research Productivity? 17:21 How Close Is an AI Research Intern? 26:06 RL Beyond Verifiable Tasks 35:38 App Companies: Build Models or Lean on Labs? 46:21 Multimodal Is Still Missing Something 49:46 OpenAI's Bet on Reasoning 55:26 The AI Coding Wars 59:26 Focus vs. Keeping Embers Burning 1:02:09 Open Source vs. Closed Source Gap 1:05:15 Quickfire YouTube: Spotify: Apple:

I sat down with Lukasz Kaiser to get into whether the architecture he helped invent is actually enough, and what's next in generalization, coding agents, RL and more. Lukasz co-authored "Attention Is All You Need," the paper that introduced the transformer and worked on reasoning models at OpenAI so he’s been a key part of major shifts in the field. We hit on: ▪️ The case for and against a new architecture coming after the transformer ▪️ What’s required for model generalization in the physical world ▪️ How much coding agents have improved his AI research productivity ▪️ The next domains for RL ▪️ Why Anthropic initially won coding ▪️ Future research directions he’s excited about 0:00 Intro 1:12 Transformers vs. Human Learning 8:37 How Do We Get Physical World Generalization? 10:52 What Comes After Transformers 13:59 How Much Have Agents Improved Lukasz's AI Research Productivity? 17:21 How Close Is an AI Research Intern? 26:06 RL Beyond Verifiable Tasks 35:38 App Companies: Build Models or Lean on Labs? 46:21 Multimodal Is Still Missing Something 49:46 OpenAI's Bet on Reasoning 55:26 The AI Coding Wars 59:26 Focus vs. Keeping Embers Burning 1:02:09 Open Source vs. Closed Source Gap 1:05:15 Quickfire YouTube: Spotify: Apple:

Jacob Effron

75,222 views • 4 months ago

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

CyberRobo

17,092 views • 4 months ago