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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:

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Jacob Effron

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13,961 görüntüleme • 11 ay önce •via X (Twitter)

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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 -
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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

28,006 görüntüleme • 1 ay önce

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
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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

244,673 görüntüleme • 7 ay önce

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
44:52

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

129,723 görüntüleme • 11 ay önce

Physical Intelligence (Physical Intelligence) has raised $400 million in a new round at a $2 billion valuation. Physical Intelligence (Physical Intelligence), San Francisco, California, United States, was founded in 2024 by Adnan Esmail (Adnan Esmail), Brian Ichter (Brian Ichter), Karol Hausman (Karol Hausman), Lachy Groom (Lachy Groom), and Sergey Levine (Sergey Levine).
0:35

Physical Intelligence (Physical Intelligence) has raised $400 million in a new round at a $2 billion valuation. Physical Intelligence (Physical Intelligence), San Francisco, California, United States, was founded in 2024 by Adnan Esmail (Adnan Esmail), Brian Ichter (Brian Ichter), Karol Hausman (Karol Hausman), Lachy Groom (Lachy Groom), and Sergey Levine (Sergey Levine).

Silicon Valley Investclub

13,627 görüntüleme • 1 yıl önce

My chat with Nathan Benaich about his very impressive 2024 @stateofaireport * economics of AI foundational models * geopolitics of AI * robotics renaissance - YouTube: - Spotify: - Apple Podcasts:
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My chat with Nathan Benaich about his very impressive 2024 @stateofaireport * economics of AI foundational models * geopolitics of AI * robotics renaissance - YouTube: - Spotify: - Apple Podcasts:

Matt Turck

13,375 görüntüleme • 1 yıl önce

New Unsupervised Learning with Joshua Meier @ NeurIPS (Chai Discovery) on how AI x drug discovery is starting to inflect and: - The 3 waves of AI biotech - How AI models think differently than chemists - Building "Photoshop for molecules" - Deciding what to open source
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New Unsupervised Learning with Joshua Meier @ NeurIPS (Chai Discovery) on how AI x drug discovery is starting to inflect and: - The 3 waves of AI biotech - How AI models think differently than chemists - Building "Photoshop for molecules" - Deciding what to open source

Jacob Effron

12,351 görüntüleme • 10 ay önce

Meet Gemini Robotics: our latest AI models designed for a new generation of helpful robots. 🤖 Based on Gemini 2.0, they bring capabilities such as better reasoning, interactivity, dexterity and generalization into the physical world. 🧵
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Meet Gemini Robotics: our latest AI models designed for a new generation of helpful robots. 🤖 Based on Gemini 2.0, they bring capabilities such as better reasoning, interactivity, dexterity and generalization into the physical world. 🧵

Google DeepMind

642,242 görüntüleme • 1 yıl önce

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
1:14:20

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 görüntüleme • 3 ay önce

What’s next in robotics starts with physical AI. 🤖 By combining decades of machine data with AI, simulation, and digital twins, leaders like AGIBOT, FRANKA ROBOTICS, CaterpillarInc, and PlusAI are scaling intelligent machines across industries and redefining what robots can do in the physical world. Watch the full video 📹
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What’s next in robotics starts with physical AI. 🤖 By combining decades of machine data with AI, simulation, and digital twins, leaders like AGIBOT, FRANKA ROBOTICS, CaterpillarInc, and PlusAI are scaling intelligent machines across industries and redefining what robots can do in the physical world. Watch the full video 📹

NVIDIA Robotics

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Experience the UK’s next industrial revolution—where AI, powered by NVIDIA, is reshaping every industry. From robotics and manufacturing to quantum breakthroughs and life sciences, watch how visionaries are building tomorrow’s factories of intelligence.
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Experience the UK’s next industrial revolution—where AI, powered by NVIDIA, is reshaping every industry. From robotics and manufacturing to quantum breakthroughs and life sciences, watch how visionaries are building tomorrow’s factories of intelligence.

NVIDIA

44,358 görüntüleme • 9 ay önce

New NVIDIA physical AI agent-ready skills are changing how robotics researchers work. 🤖 With NVIDIA robotics skills, researchers can automate the most common development steps from scene preparation to simulation and learning with Omniverse libraries, Isaac frameworks, and physical AI open datasets. Specialized skills like Isaac Mobility extend that even further. Learn more ➡️
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New NVIDIA physical AI agent-ready skills are changing how robotics researchers work. 🤖 With NVIDIA robotics skills, researchers can automate the most common development steps from scene preparation to simulation and learning with Omniverse libraries, Isaac frameworks, and physical AI open datasets. Specialized skills like Isaac Mobility extend that even further. Learn more ➡️

NVIDIA Robotics

99,687 görüntüleme • 29 gün önce

Pi0 from Physical Intelligence is one of the best generalist Vision-Language-Action (VLA) models for robotics (and predecessor of Pi0.5). 🧵 Here's how it works + how you can fine-tune it on your own robot for a simple task:
1:12

Pi0 from Physical Intelligence is one of the best generalist Vision-Language-Action (VLA) models for robotics (and predecessor of Pi0.5). 🧵 Here's how it works + how you can fine-tune it on your own robot for a simple task:

Ilia

32,956 görüntüleme • 9 ay önce

Today, we're joined by Sergey Levine, associate professor at UC Berkeley EECS and co-founder of Physical Intelligence to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the model architecture, which pairs a vision language model (VLM) with a diffusion-based action expert, and the model training "recipe," emphasizing the roles of pre-training and post-training with a diverse mixture of real-world data to ensure robust and intelligent robot learning. We review the data collection approach, which uses human operators and teleoperation rigs, the potential of synthetic data and reinforcement learning in enhancing robotic capabilities, and much more. We also introduce the team’s new FAST tokenizer, which opens the door to a fully Transformer-based model and significant improvements in learning and generalization. Finally, we cover the open-sourcing of π0 and future directions for their research. 🎧 / 🎥 Listen or watch the full episode on our page: 📖 CHAPTERS =============================== 00:00 - Introduction 2:14 - Physical Intelligence 3:47 - Key challenges in robotic learning 6:13 - Reinforcement learning in π0 and robotic foundation models 8:36 - π0 VLM model architecture 15:33 - π0 model recipe 18:39 - Pre-training dataset 22:47 - Post-training 24:23 - Laundry folding demo 31:32 - Scaling laws on π0 model 34:57 - FAST 40:26 - Open sourcing π0 43:37 - Other robot types 46:27 - Future directions
52:00

Today, we're joined by Sergey Levine, associate professor at UC Berkeley EECS and co-founder of Physical Intelligence to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the model architecture, which pairs a vision language model (VLM) with a diffusion-based action expert, and the model training "recipe," emphasizing the roles of pre-training and post-training with a diverse mixture of real-world data to ensure robust and intelligent robot learning. We review the data collection approach, which uses human operators and teleoperation rigs, the potential of synthetic data and reinforcement learning in enhancing robotic capabilities, and much more. We also introduce the team’s new FAST tokenizer, which opens the door to a fully Transformer-based model and significant improvements in learning and generalization. Finally, we cover the open-sourcing of π0 and future directions for their research. 🎧 / 🎥 Listen or watch the full episode on our page: 📖 CHAPTERS =============================== 00:00 - Introduction 2:14 - Physical Intelligence 3:47 - Key challenges in robotic learning 6:13 - Reinforcement learning in π0 and robotic foundation models 8:36 - π0 VLM model architecture 15:33 - π0 model recipe 18:39 - Pre-training dataset 22:47 - Post-training 24:23 - Laundry folding demo 31:32 - Scaling laws on π0 model 34:57 - FAST 40:26 - Open sourcing π0 43:37 - Other robot types 46:27 - Future directions

The TWIML AI Podcast

19,942 görüntüleme • 1 yıl önce

We brought Dynasaur to The Clean Show and ran it live, 8 hours a day. Each deployment fuels our robotics foundation model — scaling data, accelerating iteration, and enabling real-world generalization. Embodied intelligence won’t come from lab demos, but from deployment-first robotics running continuously in the wild.
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We brought Dynasaur to The Clean Show and ran it live, 8 hours a day. Each deployment fuels our robotics foundation model — scaling data, accelerating iteration, and enabling real-world generalization. Embodied intelligence won’t come from lab demos, but from deployment-first robotics running continuously in the wild.

Dyna Robotics

24,680 görüntüleme • 10 ay önce

At Physical Intelligence, we have open roles robotics research scientist and research engineer. If you love robots, hands-on experimental robotics work, and getting amazing, dexterous skills to work, get in touch! See
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At Physical Intelligence, we have open roles robotics research scientist and research engineer. If you love robots, hands-on experimental robotics work, and getting amazing, dexterous skills to work, get in touch! See

Sergey Levine

26,520 görüntüleme • 1 yıl önce

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
43:58

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 görüntüleme • 8 ay önce

new No Priors with the legendary Fei-Fei Li! topics: why she’s building world labs missing gaps in the AI future robotics and physical intelligence greatest challenges of 3D her PhD, ImageNet advising sensei Karpathy importance of human-centered AI
0:25

new No Priors with the legendary Fei-Fei Li! topics: why she’s building world labs missing gaps in the AI future robotics and physical intelligence greatest challenges of 3D her PhD, ImageNet advising sensei Karpathy importance of human-centered AI

sarah guo // conviction

11,251 görüntüleme • 1 yıl önce

Reborn CEO Luffy Yu says the core bottleneck in robotics foundation models compared to LLMs is the lack of a permissionless, scrapable, open-source ‘Physical Internet.’
1:38

Reborn CEO Luffy Yu says the core bottleneck in robotics foundation models compared to LLMs is the lack of a permissionless, scrapable, open-source ‘Physical Internet.’

The Humanoid Hub

31,372 görüntüleme • 1 yıl önce

Singularity in Robotics: What It Is and How to Design Around It 🤖 New podcast episode with Scott Walter Humanoid Botangelist Watch video here: Spotify: #robotics
2:37

Singularity in Robotics: What It Is and How to Design Around It 🤖 New podcast episode with Scott Walter Humanoid Botangelist Watch video here: Spotify: #robotics

Marwa ElDiwiny

12,360 görüntüleme • 1 yıl önce

Jensen Huang says the industry is shifting from generative AI to agentic AI The next major step is fusing public cloud frontier models with customized open-source systems running on enterprise servers But the end goal is physical: "moving intelligence from the cloud into industrial AI, factories and robotics"
1:25

Jensen Huang says the industry is shifting from generative AI to agentic AI The next major step is fusing public cloud frontier models with customized open-source systems running on enterprise servers But the end goal is physical: "moving intelligence from the cloud into industrial AI, factories and robotics"

Haider.

49,411 görüntüleme • 6 ay önce