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Biological intelligence to bootstrap robotic intelligence? If robotics is about to enter its golden age, Mecka is one of the companies feeding it, by sensorizing humans to train robots. The biggest challenge robotics faces is the data flywheel cold start problem. LLMs got a free cold start from the...

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Sonya Huang 🐥

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35,704 görüntüleme • 14 gün önce •via X (Twitter)

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Clementine, like the fruit profil fotoğrafı
Clementine, like the fruit14 gün önce

@MeckaAI @joshavata not a second during this interview where you weren’t smiling. love seeing ceo’s love their work

Sonya Huang 🐥 profil fotoğrafı
Sonya Huang 🐥14 gün önce

Full episode on podcast platforms: YouTube: Spotify: Apple:

Anas Biad profil fotoğrafı
Anas Biad14 gün önce

@MeckaAI @joshavata the real question is are we going to have Gundams or not?

Ty profil fotoğrafı
Ty14 gün önce

@MeckaAI Robot Dog Panther Horse FTW!!!

Rushabh Sheth profil fotoğrafı
Rushabh Sheth13 gün önce

@MeckaAI The cold start is the crux. Actually putting sensors on people is a moat in itself.

George Burke profil fotoğrafı
George Burke14 gün önce

@MeckaAI Love that they built gloves for the force data. My hunch is the last mile still has to be learned on the robot itself, on the job, since a human hand and a gripper feel that egg very differently.

nabrih77 profil fotoğrafı
nabrih7714 gün önce

@MeckaAI Bullish on @MeckaAI Big W @joshavata @jasontheutopian i see mecka funding $8M first and now $128M+ LFG

大姚 profil fotoğrafı
大姚14 gün önce

@MeckaAI Robot data cold-start is the real bottleneck — sensorizing humans is a clever bypass

David Arnal profil fotoğrafı
David Arnal14 gün önce

@MeckaAI First-person video captures intent, but force data seems just as critical. Knowing what a task looks like is very different from knowing how to handle fragile objects safely.

DexorynLabs profil fotoğrafı
DexorynLabs13 gün önce

@MeckaAI Solving cold starts unlocks velocity.

Floryn profil fotoğrafı
Floryn14 gün önce

@MeckaAI power boost, we need that too

sasaki kazuya profil fotoğrafı
sasaki kazuya13 gün önce

@MeckaAI Human-generated data could be the missing link for robotics, turning everyday movement into training signals and helping machines learn faster in the real world

vøv △ profil fotoğrafı
vøv △13 gün önce

@pythianism @MeckaAI Millions of hours, robot brain finally wakes

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1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

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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
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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

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Physical Intelligence (Physical Intelligence) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection. In this episode of the Lightcone Podcast , co-founder Quan Vuong (Quan Vuong) sat down with Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies. 00:00 — Robotics just got cheaper 00:41 — The GPT moment for robotics 02:24 — Why robots didn’t work before 05:30 — The breakthrough that changed everything 09:12 — The data problem 13:33 — Robots learning without data 15:05 — Robots folding laundry (for real) 22:18 — From engineering problem → ops problem 29:12 — The startup playbook 38:46 — Thousands of robotics startups are coming
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Physical Intelligence (Physical Intelligence) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection. In this episode of the Lightcone Podcast , co-founder Quan Vuong (Quan Vuong) sat down with Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies. 00:00 — Robotics just got cheaper 00:41 — The GPT moment for robotics 02:24 — Why robots didn’t work before 05:30 — The breakthrough that changed everything 09:12 — The data problem 13:33 — Robots learning without data 15:05 — Robots folding laundry (for real) 22:18 — From engineering problem → ops problem 29:12 — The startup playbook 38:46 — Thousands of robotics startups are coming

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Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics
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Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics

MTS

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The scarce thing in a data center is not manpower, but instinct that only comes from years on the floor. Kingston Kuan and I spent the past month with data center operators and industrial robotics startups. Most robotics companies are focused on robots as a productivity amplifiers: 24/7 uptime, five days of work done in two. Few are focused on the potential of robots to change how people work altogether. We want to show what it looks like to rethink human-robot collaboration, using AI so a shrinking pool of experts can meet the increasing demands of future infrastructure.
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The scarce thing in a data center is not manpower, but instinct that only comes from years on the floor. Kingston Kuan and I spent the past month with data center operators and industrial robotics startups. Most robotics companies are focused on robots as a productivity amplifiers: 24/7 uptime, five days of work done in two. Few are focused on the potential of robots to change how people work altogether. We want to show what it looks like to rethink human-robot collaboration, using AI so a shrinking pool of experts can meet the increasing demands of future infrastructure.

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JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado
1:49

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

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“Data in robotics versus data in LLMs has a very different alpha decay problem.” — Tarun Chitra, Managing Partner Robot Ventures Much of what LLMs learn can carry across contexts. Robotics data is far more conditional: what works depends heavily on the robot, the objects, and the environment. As robots move into more complex, real-world settings, previously collected data can become less relevant much faster. For builders and investors, that shifts the question from “How much data do you have” to “How quickly can you close the loop between deployment, new data, and adaptation”. From The Convergence: Bhutan by YZi Labs.
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“Data in robotics versus data in LLMs has a very different alpha decay problem.” — Tarun Chitra, Managing Partner Robot Ventures Much of what LLMs learn can carry across contexts. Robotics data is far more conditional: what works depends heavily on the robot, the objects, and the environment. As robots move into more complex, real-world settings, previously collected data can become less relevant much faster. For builders and investors, that shifts the question from “How much data do you have” to “How quickly can you close the loop between deployment, new data, and adaptation”. From The Convergence: Bhutan by YZi Labs.

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German robotics company NEURA Robotics has created NEURA Gym: a large-scale, physical AI gym and training ground where hundreds of robots, including the humanoid 4NE-1, learn through real-world interactions. The company is collaborating with the Neuraverse ecosystem to create the world's largest repository of physical training data, promoting the application of humanoid robots.
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German robotics company NEURA Robotics has created NEURA Gym: a large-scale, physical AI gym and training ground where hundreds of robots, including the humanoid 4NE-1, learn through real-world interactions. The company is collaborating with the Neuraverse ecosystem to create the world's largest repository of physical training data, promoting the application of humanoid robots.

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Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:
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Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

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“The universe is really big, and almost none of it is inside computers.” When a problem is hard, make it bigger. Sergey Levine put a room full of robots in a repurposed Google conference room and had them grasp objects all day. It worked. Then it hit a wall: lots of experience, no prior knowledge. That wall became Physical Intelligence. This conversation at South Park Commons was awesome. I learned a lot! (00:28) From Video Games to Robotics: Sergey Levine's Journey (04:24) Founding Physical Intelligence: Why the Timing and Team Mattered (06:30) Inside Pi Zero: Building a Foundation Model for Robots (09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize (16:45) What Makes a Great Robotics Researcher? (21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops (28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots (33:24) World Models, Data Strategy, and Building a Robotics Research Culture
42:23

“The universe is really big, and almost none of it is inside computers.” When a problem is hard, make it bigger. Sergey Levine put a room full of robots in a repurposed Google conference room and had them grasp objects all day. It worked. Then it hit a wall: lots of experience, no prior knowledge. That wall became Physical Intelligence. This conversation at South Park Commons was awesome. I learned a lot! (00:28) From Video Games to Robotics: Sergey Levine's Journey (04:24) Founding Physical Intelligence: Why the Timing and Team Mattered (06:30) Inside Pi Zero: Building a Foundation Model for Robots (09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize (16:45) What Makes a Great Robotics Researcher? (21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops (28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots (33:24) World Models, Data Strategy, and Building a Robotics Research Culture

Aditya Agarwal

53,808 görüntüleme • 12 gün önce

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️
29:39

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

Axis Robotics

23,713 görüntüleme • 2 ay önce

There are over 90 robot gyms launching in China right now. 100+ robots per center. We are in the robot data gold rush. China's humanoid makers now ship 97% of all humanoids globally. The central government just put $138 billion into a robotics venture fund. And this is only the tip of the iceberg. Robots need an ungodly amount of data to work in the real world. The biggest bottleneck isn't hardware, it's data. So a whole industry appeared overnight to feed them. Welcome to our generation's gold rush: the robot data gold rush. We made a full video on it. We cover why it's such an insane business, how it actually works, the shady stuff (yes, sweatshops for robot data are a thing), and why this might be the completely wrong approach.
17:42

There are over 90 robot gyms launching in China right now. 100+ robots per center. We are in the robot data gold rush. China's humanoid makers now ship 97% of all humanoids globally. The central government just put $138 billion into a robotics venture fund. And this is only the tip of the iceberg. Robots need an ungodly amount of data to work in the real world. The biggest bottleneck isn't hardware, it's data. So a whole industry appeared overnight to feed them. Welcome to our generation's gold rush: the robot data gold rush. We made a full video on it. We cover why it's such an insane business, how it actually works, the shady stuff (yes, sweatshops for robot data are a thing), and why this might be the completely wrong approach.

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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
58:17

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

Y Combinator

114,136 görüntüleme • 2 ay önce

Robotics has a data problem, and egocentric data is a compelling way to solve it because egocentric data is (1) scalable — it’s cheap to collect — and (2) easily captures the breadth and diversity of real human tasks, while (3) being true to real physics. Lightwheel recently released a hundred thousand hours of high-quality egocentric video data, spread across a wide range of human tasks from cleaning and tidying to assembly and construction to retail tasks. Here to tell us about this tremendous effort is Steve Xie, CEO and co-founder of Lightwheel. The data is also available on HuggingFace right now. Watch Episode 102 of RoboPapers, with Michael Cho - Rbt/Acc and Jiafei Duan, to learn more!
1:09:08

Robotics has a data problem, and egocentric data is a compelling way to solve it because egocentric data is (1) scalable — it’s cheap to collect — and (2) easily captures the breadth and diversity of real human tasks, while (3) being true to real physics. Lightwheel recently released a hundred thousand hours of high-quality egocentric video data, spread across a wide range of human tasks from cleaning and tidying to assembly and construction to retail tasks. Here to tell us about this tremendous effort is Steve Xie, CEO and co-founder of Lightwheel. The data is also available on HuggingFace right now. Watch Episode 102 of RoboPapers, with Michael Cho - Rbt/Acc and Jiafei Duan, to learn more!

RoboPapers

33,675 görüntüleme • 1 ay önce

AlphaGo: two years, human data, humans in the loop. AlphaZero: gave up the human data and outperformed all of it in a few hours. Four hours to beat the best chess AI ever built. David Silver on why Ineffable Intelligence is making the same bet again.
1:37

AlphaGo: two years, human data, humans in the loop. AlphaZero: gave up the human data and outperformed all of it in a few hours. Four hours to beat the best chess AI ever built. David Silver on why Ineffable Intelligence is making the same bet again.

Sequoia Capital

11,242 görüntüleme • 21 gün önce

One hundred thousand hours of high-quality egocentric data from Steve Xie and the Lightwheel team. A huge contribution to the community and a big step for embodied AI. it truly seems as if the end of the robotics data shortage is in sight -- which totally changes how we can think about problems vs just a couple years ago. More on RoboPapers ->
0:30

One hundred thousand hours of high-quality egocentric data from Steve Xie and the Lightwheel team. A huge contribution to the community and a big step for embodied AI. it truly seems as if the end of the robotics data shortage is in sight -- which totally changes how we can think about problems vs just a couple years ago. More on RoboPapers ->

Chris Paxton

11,527 görüntüleme • 1 ay 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 • 7 ay önce

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 görüntüleme • 4 ay önce

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
1:15:15

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

Patrick OShaughnessy

134,397 görüntüleme • 7 ay önce