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We profiled the most hyped robotics company in the world. Inside Physical Intelligence with Co-founder Lachy Groom Exclusive interview in the Physical Intelligence robotics lab, who’re backed by top investors to build robots which work in the real world. Not just scripted environments. We cover: 0:00 So…What is Physical... Intelligence? 1:41 Why can’t we solve Marovec’s Paradox? 2:33 Live Demo: Meet the Machines 4:17 Why robots still struggle today… 6:14 Meet PI’s Investors (Thrive Capital) 7:19 Teaming up with the mega-brilliant co-founder group of Karol Haussman, Sergey Levine, Chelsea Finn, Brian Ichter, Adnan Esmail & Quan Vuong 9:20 PI’s Robot Capabilities 11:37 How are they making intelligent robots? 13:43 The Future Impact of Everyday Robots 15:49 Jeff Bezos Investment in Physical Intelligence 16:43 What will the next 1-3 years look like? 17:54 Revealing PI’s Work Culture 19:23 Outroshow more

Sachin Shah

4,834 subscribers

119,642 次观看 • 8 个月前 •via X (Twitter)

教育新闻政治科学技术

Anya Rossi• Live Now

Private livecam show

24 条评论

Alex Brogan 的头像

Alex Brogan8 个月前

@lachygroom A++++

Robert Scoble 的头像

Robert Scoble8 个月前

@lachygroom Awesome, thanks. I hear @1x_tech has a better world model.

Hari 的头像

Hari8 个月前

@lachygroom Sergey is incredible–super exciting company!

Shikima 的头像

Shikima8 个月前

@lachygroom calling out Moravec’s Paradox 👍 perception + dexterity is the real battle props to @Sachin_Shah_ for the deep dive If these guys crack general-purpose robot brains in the next 1–3 years, then our laundry might fold itself watching how this space unfolds 👀

Nikunj Kothari 的头像

Nikunj Kothari8 个月前

@lachygroom Epic

Sachin Shah 的头像

Sachin Shah8 个月前

@lachygroom hope you enjoy it!

Nikunj Kothari 的头像

Nikunj Kothari8 个月前

@lachygroom Lachy sends me enough texts - not sure if I have the room in my brain to hear him for 20 minutes :P

Ethan Choi 的头像

Ethan Choi8 个月前

@lachygroom @lachygroom is a top bloke :) they’re building something special at PI

JD Ross 的头像

JD Ross8 个月前

@lachygroom building the things building the future

interstellar travel. 的头像

interstellar travel.8 个月前

@vkhosla @lachygroom 3.14!

Adam Miller 的头像

Adam Miller8 个月前

@lachygroom best profile to date

Nick Mason 的头像

Nick Mason8 个月前

@lachygroom Thinking on your feet, literally.

sracha 的头像

sracha8 个月前

@lachygroom documentary style so good

Kaveri Engine 的头像

Kaveri Engine8 个月前

@lachygroom OpenAI of robotics!!

Faraz 🇺🇸 🚀 的头像

Faraz 🇺🇸 🚀8 个月前

@lachygroom I visited last year. Very impressive.

Jan Olsson 的头像

Jan Olsson8 个月前

@lachygroom Great profile!

Tom Duffy 的头像

Tom Duffy8 个月前

@lachygroom 👏👏

Nyiko 的头像

Nyiko8 个月前

@lachygroom intelligence is not limited to the brain - it's literally at the cellular level & maybe beyond that - look up the synthetic biologist Michael Levin

The AI Index 的头像

The AI Index8 个月前

@lachygroom Real world robotics is where hype goes to get humbled, curious if Physical Intelligence actually delivers

Kyle 的头像

Kyle8 个月前

@lachygroom Amazing video! I'm a beverage manufacturer business owner in Melb Australia. Would love to show you around!

David Liu 的头像

David Liu8 个月前

@lachygroom fond memories of that building and neighborhood - lots of hardware and building!

Jonathan Shriftman 的头像

Jonathan Shriftman8 个月前

Finally got around to watching this. Elegant framing on the challenge of physical AI @lachygroom "What’s been easy for computers is things that are hard for us, like arithmetic reasoning.And what’s been very hard for computers is what’s trivial for us, walking, driving, learning to play video games. And what’s really been missing is physical intelligence, is the brain that can operate these robots." Also fascinating to hear about starting w/ laundry

Skyta Space 的头像

Skyta Space8 个月前

@lachygroom Physical Intelligence tackling general manipulation instead of narrow task automation is the right approach. Scripted demos look impressive but fall apart the moment environment conditions change slightly.

Eamon Callahan 的头像

Eamon Callahan8 个月前

@lachygroom This is the side of building most people don’t see …

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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 次观看 • 6 个月前

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

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Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

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382,479 次观看 • 5 个月前

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44,534 次观看 • 2 个月前

Christine (郡主Christine (✱,✱)), co-founder of Axis Robotics spent eight years ago studying mechanical engineering and working with ABB and KUKA robots before pivoting through stablecoin payments and landing on physical AI data. Axis Robotics has raised $12M to build a distributed training engine where anyone with a browser can teach robots. The network already has 200,000+ contributors and a dataset of 50,000+ robot trajectories across 207 tasks. On today's episode: why noisy human data beats "perfect" AI data, why simulation might be the key to physical AI's GPT moment, and the uncomfortable truth about people getting paid to train the robots that'll eventually replace them. Timestamps: 0:00 Intro: Building the Data Layer Behind Physical AI 1:34 Hot Take: Noisy Data vs. Perfect Data 3:16 Why Simulation Data Will Bring the GPT Moment for Robotics 7:25 From Stablecoin Payments to Robotics Data 10:08 Why Axis Chose Data Over Building Robots 16:10 Live Demo: Training a Robot Arm Like Playing FIFA 24:02 How Contributors Get Paid (Tokens + Stablecoin) 26:42 Inside the $12M Seed Round Pitch 32:08 200K+ Contributors & Viral Growth via Base 38:38 Sim-to-Real: The Living Data Engine 41:34 Token & Web3 Plans for Axis Robotics 43:00 Rapid Fire Questions

Christine (郡主Christine (✱,✱)), co-founder of Axis Robotics spent eight years ago studying mechanical engineering and working with ABB and KUKA robots before pivoting through stablecoin payments and landing on physical AI data. Axis Robotics has raised $12M to build a distributed training engine where anyone with a browser can teach robots. The network already has 200,000+ contributors and a dataset of 50,000+ robot trajectories across 207 tasks. On today's episode: why noisy human data beats "perfect" AI data, why simulation might be the key to physical AI's GPT moment, and the uncomfortable truth about people getting paid to train the robots that'll eventually replace them. Timestamps: 0:00 Intro: Building the Data Layer Behind Physical AI 1:34 Hot Take: Noisy Data vs. Perfect Data 3:16 Why Simulation Data Will Bring the GPT Moment for Robotics 7:25 From Stablecoin Payments to Robotics Data 10:08 Why Axis Chose Data Over Building Robots 16:10 Live Demo: Training a Robot Arm Like Playing FIFA 24:02 How Contributors Get Paid (Tokens + Stablecoin) 26:42 Inside the $12M Seed Round Pitch 32:08 200K+ Contributors & Viral Growth via Base 38:38 Sim-to-Real: The Living Data Engine 41:34 Token & Web3 Plans for Axis Robotics 43:00 Rapid Fire Questions

Pukerainbow 🤮🌈

23,436 次观看 • 2 天前

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My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

The Humanoid Hub

24,313 次观看 • 2 个月前

BREAKING: Rare Interview w/ Eclipse Co-Founder & CEO Lior Susan Eclipse turned $147M into a $2.5B Cerebras stake. $12.5B AUM. 90 companies. 30 built in-house. $40B–$50B in portfolio commercial deals last year. Lior goes deep on the $100T physical-world opportunity, Eclipse’s Venture Equity model & why free cash flow beats gross margin. Fund Stats: › $1.3B raised in April across Fund VI + Early Growth III › 17x on Cerebras at the $185 IPO price › Portfolio companies raised $15.9B in 2026 alone › 30 of 90 companies built in-house through Venture Equity › $40B–$50B in commercial deals signed across the portfolio last year "We never thought of ourselves as venture capital. We call it operators with capital because we are operators with capital." Founded in 2015 by Lior Susan alongside Silicon Valley legend Pierre Lamond, who worked with Gordon Moore at Fairchild Semiconductor, helped build National Semiconductor & spent nearly 3 decades at Sequoia. Eclipse bet early on the physical world across semiconductors, manufacturing, robotics, defense, energy & infrastructure. Portfolio: Andrew Feldman x Cerebras, Parallax, Prime Minute, Anduril, Wayve, Redwood Materials, VulcanForms, Bedrock Robotics, True Anomaly, Ursa Major, Oxide, Bright Machines, Verkada, Mytra, Peak Energy, The Nuclear Company, Reliable Robotics, Skyryse, Mind Robotics & ALSO. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Lior Susan, Founder & CEO at Eclipse (01:02) The story behind Eclipse (02:35) How Lior met Pierre (05:54) The culture Pierre brought to the team (07:33) Why physical companies need operators (11:21) How Eclipse decides what to build (12:35) How Mind Robotics was spun out of Rivian (15:41) Why Lior started Eclipse (18:21) Why building physical companies is so hard (20:53) Why there are only 3 major memory companies (21:51) Why hardware can beat SaaS economics (24:33) Inside Eclipse’s physical industry bet (29:15) This is the best time to build (31:01) The Redwood Materials story (32:35) The "Second Act" of physical companies (33:20) Building moats in the Physical World (34:05) Why on-prem infrastructure is coming back (36:01) Building the "Eclipse Economy" (40:08) Why physical infrastructure creates jobs (41:19) From banana farmer to VC (43:37) What it really means to support a portfolio company (46:00) Who inspires Lior?

BREAKING: Rare Interview w/ Eclipse Co-Founder & CEO Lior Susan Eclipse turned $147M into a $2.5B Cerebras stake. $12.5B AUM. 90 companies. 30 built in-house. $40B–$50B in portfolio commercial deals last year. Lior goes deep on the $100T physical-world opportunity, Eclipse’s Venture Equity model & why free cash flow beats gross margin. Fund Stats: › $1.3B raised in April across Fund VI + Early Growth III › 17x on Cerebras at the $185 IPO price › Portfolio companies raised $15.9B in 2026 alone › 30 of 90 companies built in-house through Venture Equity › $40B–$50B in commercial deals signed across the portfolio last year "We never thought of ourselves as venture capital. We call it operators with capital because we are operators with capital." Founded in 2015 by Lior Susan alongside Silicon Valley legend Pierre Lamond, who worked with Gordon Moore at Fairchild Semiconductor, helped build National Semiconductor & spent nearly 3 decades at Sequoia. Eclipse bet early on the physical world across semiconductors, manufacturing, robotics, defense, energy & infrastructure. Portfolio: Andrew Feldman x Cerebras, Parallax, Prime Minute, Anduril, Wayve, Redwood Materials, VulcanForms, Bedrock Robotics, True Anomaly, Ursa Major, Oxide, Bright Machines, Verkada, Mytra, Peak Energy, The Nuclear Company, Reliable Robotics, Skyryse, Mind Robotics & ALSO. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Lior Susan, Founder & CEO at Eclipse (01:02) The story behind Eclipse (02:35) How Lior met Pierre (05:54) The culture Pierre brought to the team (07:33) Why physical companies need operators (11:21) How Eclipse decides what to build (12:35) How Mind Robotics was spun out of Rivian (15:41) Why Lior started Eclipse (18:21) Why building physical companies is so hard (20:53) Why there are only 3 major memory companies (21:51) Why hardware can beat SaaS economics (24:33) Inside Eclipse’s physical industry bet (29:15) This is the best time to build (31:01) The Redwood Materials story (32:35) The "Second Act" of physical companies (33:20) Building moats in the Physical World (34:05) Why on-prem infrastructure is coming back (36:01) Building the "Eclipse Economy" (40:08) Why physical infrastructure creates jobs (41:19) From banana farmer to VC (43:37) What it really means to support a portfolio company (46:00) Who inspires Lior?

Molly O’Shea

66,227 次观看 • 3 天前

Qasar Younis was COO of Y Combinator when Sam Altman was its president. Peter Ludwig was an early engineer on Android Automotive at Google. Both are Detroit guys with family roots at General Motors. In 2017, they started Applied Intuition to make a billion machines intelligent. Putting intelligence into a machine is one of the hardest problems in technology. A car, a tractor, and a mining vehicle each have different hardware, different sensors, and lives on the line if the software fails. So instead of building one machine, they built the technology that goes into all of them. Cars, trucks, defense vehicles, mining equipment, combines, robots. Like Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence. They are the only company in the world that does this across all of these industries. They have raised about a billion dollars. They have never spent any of it. Qasar believes that when we look back 25 years from now, the most important companies in the world will all be physical AI companies. Last week they launched Dana, an agent platform built to get there faster. Enjoy! Qasar Younis Applied Intuition Timestamps: 0:00 Intro 4:08 Why Physical AI Will Be Enormous 5:54 What Applied Intuition Actually Builds 9:25 Timing the Autonomous Revolution 13:59 From Tools to a Full AI Stack 21:40 Why Chatbots Can’t Build Robots 25:18 The Physical AI Data Flywheel 28:19 The Moat Behind Machine Intelligence 30:13 What’s Slowing Physical AI Down? 32:30 Applied Intuition’s Business Model 36:10 Competing in a Massive Market 39:24 The Hardware Renaissance 41:09 Why They Raised $1 Billion 43:49 A World Filled With Intelligent Machines

Qasar Younis was COO of Y Combinator when Sam Altman was its president. Peter Ludwig was an early engineer on Android Automotive at Google. Both are Detroit guys with family roots at General Motors. In 2017, they started Applied Intuition to make a billion machines intelligent. Putting intelligence into a machine is one of the hardest problems in technology. A car, a tractor, and a mining vehicle each have different hardware, different sensors, and lives on the line if the software fails. So instead of building one machine, they built the technology that goes into all of them. Cars, trucks, defense vehicles, mining equipment, combines, robots. Like Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence. They are the only company in the world that does this across all of these industries. They have raised about a billion dollars. They have never spent any of it. Qasar believes that when we look back 25 years from now, the most important companies in the world will all be physical AI companies. Last week they launched Dana, an agent platform built to get there faster. Enjoy! Qasar Younis Applied Intuition Timestamps: 0:00 Intro 4:08 Why Physical AI Will Be Enormous 5:54 What Applied Intuition Actually Builds 9:25 Timing the Autonomous Revolution 13:59 From Tools to a Full AI Stack 21:40 Why Chatbots Can’t Build Robots 25:18 The Physical AI Data Flywheel 28:19 The Moat Behind Machine Intelligence 30:13 What’s Slowing Physical AI Down? 32:30 Applied Intuition’s Business Model 36:10 Competing in a Massive Market 39:24 The Hardware Renaissance 41:09 Why They Raised $1 Billion 43:49 A World Filled With Intelligent Machines

Business Breakdowns

28,566 次观看 • 2 个月前

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

70,752 次观看 • 1 个月前

One of the biggest surprises in AI over the last few years has been how well coding agents generalize beyond software. In a recent essay, MIT professor Philip Isola argued that we may be entering the era of robot-use agents: general-purpose models that can control different robots, write policies, and learn new physical tasks with little or no robot-specific training. In this episode of Decoded, we're joined by the founders of Waddle Labs and Robocurve PBC, two of the startups whose work helped drive this realization. They're working at the frontier of using general-purpose models to control robots, and their recent demos helped inspire the growing conversation around robot-use agents. Together, we dig into the research behind that idea, from code-as-policies and vision-language-action models to the harnesses and evals needed to make these systems work in the real world. 00:00 — What Are Robot-Use Agents? 02:11 — From Vision-Language-Action Models to General LLMs 04:07 — The Bitter Lesson for Robotics 07:04 — How Coding Agents Learned to Control Robots 10:41 — How Robots Learn From Experience 14:22 — The Harness as a Form of Robot Intelligence 16:14 — Watching Astra Control a Robot 20:55 — Why General Models May Win in Robotics 26:05 — How Close Are General-Purpose Robots?

One of the biggest surprises in AI over the last few years has been how well coding agents generalize beyond software. In a recent essay, MIT professor Philip Isola argued that we may be entering the era of robot-use agents: general-purpose models that can control different robots, write policies, and learn new physical tasks with little or no robot-specific training. In this episode of Decoded, we're joined by the founders of Waddle Labs and Robocurve PBC, two of the startups whose work helped drive this realization. They're working at the frontier of using general-purpose models to control robots, and their recent demos helped inspire the growing conversation around robot-use agents. Together, we dig into the research behind that idea, from code-as-policies and vision-language-action models to the harnesses and evals needed to make these systems work in the real world. 00:00 — What Are Robot-Use Agents? 02:11 — From Vision-Language-Action Models to General LLMs 04:07 — The Bitter Lesson for Robotics 07:04 — How Coding Agents Learned to Control Robots 10:41 — How Robots Learn From Experience 14:22 — The Harness as a Form of Robot Intelligence 16:14 — Watching Astra Control a Robot 20:55 — Why General Models May Win in Robotics 26:05 — How Close Are General-Purpose Robots?

Y Combinator

79,324 次观看 • 2 天前

Not enough people are talking about physical AI and robotics. I sat down with Andrew Kang, one of the best investors of the last decade, to discuss his massive bet on humanoids and robotics. He breaks down the industry, the addressable market, multiple leading companies, and why he launched a publicly-traded fund ($BOT) focused on investing in the top private robotics companies. YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:28 - Why Andrew shifted from crypto to humanoid robots 3:58 - How big is the total addressable market? 8:08 - Building conviction — the $19M bet on Figure AI 16:06 - US vs. China — who wins the robot race? 28:08 - General purpose vs. specialized robots 31:05 - Where does training data come from? 40:24 - Humanoid robots in your everyday life 43:27 - Can Tesla & Elon win the humanoid race? 46:19 - Job displacement & UBI 51:15 - RoboStrategy — the publicly traded venture fund 1:11:17 - What is exciting about Apptronik? 1:13:24 - Addressing the critics

Not enough people are talking about physical AI and robotics. I sat down with Andrew Kang, one of the best investors of the last decade, to discuss his massive bet on humanoids and robotics. He breaks down the industry, the addressable market, multiple leading companies, and why he launched a publicly-traded fund ($BOT) focused on investing in the top private robotics companies. YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:28 - Why Andrew shifted from crypto to humanoid robots 3:58 - How big is the total addressable market? 8:08 - Building conviction — the $19M bet on Figure AI 16:06 - US vs. China — who wins the robot race? 28:08 - General purpose vs. specialized robots 31:05 - Where does training data come from? 40:24 - Humanoid robots in your everyday life 43:27 - Can Tesla & Elon win the humanoid race? 46:19 - Job displacement & UBI 51:15 - RoboStrategy — the publicly traded venture fund 1:11:17 - What is exciting about Apptronik? 1:13:24 - Addressing the critics

Anthony Pompliano 🌪

682,316 次观看 • 3 个月前

The world today is built for humans, & cities built for cars... The world tomorrow will be built around the evolution of robotics. It was a pleasure hosting this week's Robotics episode of the Super Cycle Pod with Michael Cho - Rbt/Acc (BitRobot 🦾) & Nils of (Auki) have packed this full of insanely valuable insights: 0:00 - Intro + wen terminator? 5:13 - Robotics vs "GPT moment" 7:08 - Privacy in the world of robotics 11:40 - Web3 robotics + intro to frodobots / bitrobot 14:50 - Impact of $HNT on Michael and Nils 17:06 - Intro to $AUKI 19:26 - Why does dePIN as an investable asset suck? 25:36 - $AUKI community building philosophy 28:30 - Bitrobot mission w/ PhD researchers 31:40 - Bitrobot subnet structure 35:28 - Bitrobot value accrual + Impact 39:40 - $AUKI and proving people wrong 42:20 - Robotics is the biggest industry in the world 50:16 - Dangers of physical AI 55:25 - Human Philosophy in Physical AI 57:05 - How cities will change w/ robots 1:03:20 - Skills of the future 1:06:32 - Responsibility as innovators in robotics Where do you think we are in the robotics cycle? My DMs are open to teams wanting to chat about what they're building.

The world today is built for humans, & cities built for cars... The world tomorrow will be built around the evolution of robotics. It was a pleasure hosting this week's Robotics episode of the Super Cycle Pod with Michael Cho - Rbt/Acc (BitRobot 🦾) & Nils of (Auki) have packed this full of insanely valuable insights: 0:00 - Intro + wen terminator? 5:13 - Robotics vs "GPT moment" 7:08 - Privacy in the world of robotics 11:40 - Web3 robotics + intro to frodobots / bitrobot 14:50 - Impact of $HNT on Michael and Nils 17:06 - Intro to $AUKI 19:26 - Why does dePIN as an investable asset suck? 25:36 - $AUKI community building philosophy 28:30 - Bitrobot mission w/ PhD researchers 31:40 - Bitrobot subnet structure 35:28 - Bitrobot value accrual + Impact 39:40 - $AUKI and proving people wrong 42:20 - Robotics is the biggest industry in the world 50:16 - Dangers of physical AI 55:25 - Human Philosophy in Physical AI 57:05 - How cities will change w/ robots 1:03:20 - Skills of the future 1:06:32 - Responsibility as innovators in robotics Where do you think we are in the robotics cycle? My DMs are open to teams wanting to chat about what they're building.

0xSammy

60,544 次观看 • 10 个月前

Here's my conversation with Marc Raibert, founder and long-time CEO of Boston Dynamics, and head of their new AI Institute, all about the history and future of robotics. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:43 - Early robots 6:47 - Legged robots 25:27 - Boston Dynamics 28:45 - BigDog 36:52 - Hydraulic actuation 38:44 - Natural movement 44:31 - Leg Lab 51:23 - AI Institute 54:41 - Athletic intelligence 1:02:35 - Building a team 1:05:37 - Videos 1:13:25 - Engineering 1:16:53 - Dancing robots 1:21:40 - Hiring 1:25:32 - Optimus robot 1:34:02 - Future of robotics 1:38:56 - Advice for young people

Here's my conversation with Marc Raibert, founder and long-time CEO of Boston Dynamics, and head of their new AI Institute, all about the history and future of robotics. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:43 - Early robots 6:47 - Legged robots 25:27 - Boston Dynamics 28:45 - BigDog 36:52 - Hydraulic actuation 38:44 - Natural movement 44:31 - Leg Lab 51:23 - AI Institute 54:41 - Athletic intelligence 1:02:35 - Building a team 1:05:37 - Videos 1:13:25 - Engineering 1:16:53 - Dancing robots 1:21:40 - Hiring 1:25:32 - Optimus robot 1:34:02 - Future of robotics 1:38:56 - Advice for young people

Lex Fridman

484,938 次观看 • 2 年前

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

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

Andreas Klinger 🦾

14,761 次观看 • 6 个月前

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

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

122,861 次观看 • 7 个月前