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Robots don’t learn the physical world from clean datasets. They learn from messy decisions, different movements, and thousands of possible ways a task can go wrong. That’s why I think the data problem in Physical AI is much bigger than simply collecting more examples. A robot can look impressive... in a controlled demonstration and still struggle when the environment changes. A different angle. A different object. A slightly different position. A movement that doesn’t go exactly as expected. Humans handle these variations naturally because we have years of physical experience to draw from. Robots don’t. They need enormous amounts of training experience to learn how to perceive situations, choose actions, recover from mistakes, and repeat successful behavior. This is where Axis Robotics is taking an interesting approach. Instead of treating simulation as just a place to test robots, Axis is turning it into a data-generation environment for physical intelligence. More than 200K people are now contributing demonstrations in simulation, creating examples of how tasks can be performed. But the important part isn’t simply the number of contributors. It’s what happens to those demonstrations afterward. The interactions are recorded, validated, and written to Base, creating a verifiable trail around the data being generated. That gives the whole process a different structure: Humans demonstrate → simulations capture → data gets validated → training signals accumulate → robots get more experience. And that feedback loop is what interests me. Because scaling Physical AI may not come from finding one perfect algorithm. It may come from building a system capable of producing millions of useful experiences that algorithms can actually learn from. The robot is only as good as the experience available to train it. And if Axis can keep scaling the quality and volume of that experience, it could become one of the important infrastructure layers behind the next generation of capable robots. The race in Physical AI isn’t only about building smarter models. It’s about giving those models enough real-world experience to become smart in the first place. Axis Robotics.show more

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33 Kommentare

Profilbild von AkashX

AkashXvor 1 Tag

axis is onto something cool with that approach

Profilbild von Amor

Amorvor 1 Tag

Simulation turns physical experience into scalable data

Profilbild von Khing Ladipoe

Khing Ladipoevor 1 Tag

the product has a pretty clear reason to exist.

Profilbild von RegularWeb3Guy ♑️

RegularWeb3Guy ♑️vor 1 Tag

Impressive approach on this

Profilbild von Tarek_web3

Tarek_web3vor 1 Tag

This is a thoughtful perspective, and I can definitely see your point.

Profilbild von Lee

Leevor 1 Tag

Experience adds depth to otherwise ordinary moments.

Profilbild von Safiar

Safiarvor 1 Tag

The real advantage is giving robots more varied experience to learn from

Profilbild von Crypto Xenesis

Crypto Xenesisvor 1 Tag

Simulation driven data could be the key to scaling physical AI training

Profilbild von 0xZpher

0xZphervor 1 Tag

yeah, sim2real is the real data bottleneck

Profilbild von The Data girl💕🩺

The Data girl💕🩺vor 1 Tag

Keep building with axisrobotics mate.

Profilbild von 𝓐𝓭𝓮𝓵𝓮🚢

𝓐𝓭𝓮𝓵𝓮🚢vor 1 Tag

This deserves more attention.

Profilbild von Ameen

Ameenvor 1 Tag

The architecture looks worth examining

Profilbild von Big Favy

Big Favyvor 1 Tag

The execution here is what stands out.

Profilbild von EKO

EKOvor 1 Tag

How does the robot handle the thousands of possible ways a task can go wrong in messy realworld scenarios?

Profilbild von De~Dentist

De~Dentistvor 1 Tag

Looking very good here

Profilbild von Izuchukwu Daniel Obiagwu

Izuchukwu Daniel Obiagwuvor 1 Tag

Being early doesn’t automatically mean being right.

Profilbild von Jinn base.eth

Jinn base.ethvor 1 Tag

messy data is the real teacher for robots not clean datasets

Profilbild von Shimbil

Shimbilvor 1 Tag

robots only as smart as experience

Profilbild von Tarik_Defi

Tarik_Defivor 1 Tag

The real advantage is scaling diverse physical experience so robots can learn from more than perfect demonstrations

Profilbild von Dung Vu

Dung Vuvor 1 Tag

How do you envision scaling data collection for such messy real‑world scenarios?

Profilbild von 𝑺𝑯𝑨𝑹𝑰𝑭𝑼𝑳

𝑺𝑯𝑨𝑹𝑰𝑭𝑼𝑳vor 1 Tag

Totally agree that physical AI needs a deeper understanding beyond just clean data

Profilbild von MARKHOR 🐐

MARKHOR 🐐vor 1 Tag

sim data flywheel for physical ai

Profilbild von I'M 𝐄𝐌𝐎𝐍

I'M 𝐄𝐌𝐎𝐍vor 1 Tag

Axis is tackling the real Physical AI bottleneck: scalable, verifiable training experience for smarter robots.

Profilbild von Nick

Nickvor 1 Tag

Scaling experience, not just data, is the real bottleneck.

Profilbild von Ikramul Gazi

Ikramul Gazivor 1 Tag

That messiness is the real training signal, not the clean demos.

Profilbild von sweeT.moon

sweeT.moonvor 1 Tag

you envision scaling data collection for such messy real‑world scenarios?

Profilbild von Jaouad 

Jaouad vor 1 Tag

robots need real-world experience, not just data

Profilbild von MATK!NG`$™ 👑 ♣️

MATK!NG`$™ 👑 ♣️vor 1 Tag

Quality training data and continuous feedback could be the real foundation of Physical AI.

Profilbild von Monkey D. Rupiah

Monkey D. Rupiahvor 1 Tag

Simulation becomes more valuable when it generates meaningful experiences for training physical intelligence

Profilbild von Warrior Raja Mahar ⚜

Warrior Raja Mahar ⚜vor 1 Tag

Better training better data better AI solution.

Profilbild von GrimReaper023

GrimReaper023vor 1 Tag

Messy decisions teach robots more than clean datasets

Profilbild von John Mark💫

John Mark💫vor 1 Tag

The robotics movement from axisrobotics is impressive

Profilbild von ƁĘŊŤØ§

ƁĘŊŤØ§vor 1 Tag

Really impressed by what they're building. Looking forward to seeing what's next.

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1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

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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

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

a16z

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Elon just dropped a MAJOR nugget on how Tesla is going to be training Optimus to do real world tasks. They are building an Optimus Academy, which is a large scale, dedicated real-world training facility to accelerate the development of Optimus. The Academy will deploy thousands of Optimus units, potentially 10,000 to 30,000 robots, in a controlled realistic environment where they perform self-play, experiment with tasks, iterate on behaviors, and continuously generate training data through trial and error. The Tesla bots will also run millions of simulations in Tesla’s high-fidelity physics-accurate engine, allowing Optimus to close the “sim-to-real gap” by using these real-world observations to refine and validate the simulations! “You’re actually highlighting an important limitation and difference from cars. We’ll soon have 10 million cars on the road. It’s hard to duplicate that massive training flywheel. For the robot, what we’re going to need to do is build a lot of robots and put them in kind of an Optimus Academy so they can do self-play in reality. We’re actually building that out. We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks. Tesla has quite a good reality generator, a physics-accurate reality generator, that we made for the cars. We’ll do the same thing for the robots. We actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. You can do millions of simulated robots in the simulated world. You use the tens of thousands of robots in the real world to close the simulation to reality gap. Close the sim-to-real gap.”

Teslaconomics

42,563 Aufrufe • vor 7 Monaten

Elon Musk explains why AI and robots are the future of the economy? “You can think of the economy as digital and physical intelligence. AI is advancing very rapidly, but it is still somewhat confined to the digital realm. You need end effectors in the form of humanoid robots. Then you can go from intelligence manifesting itself digitally to shaping atoms. An economy is the production of goods and the provision of services. If you have vast numbers of robots with vast amounts of digital intelligence, you have a quasi-infinite economy.”

Elon Musk explains why AI and robots are the future of the economy? “You can think of the economy as digital and physical intelligence. AI is advancing very rapidly, but it is still somewhat confined to the digital realm. You need end effectors in the form of humanoid robots. Then you can go from intelligence manifesting itself digitally to shaping atoms. An economy is the production of goods and the provision of services. If you have vast numbers of robots with vast amounts of digital intelligence, you have a quasi-infinite economy.”

DogeDesigner

44,728 Aufrufe • vor 1 Monat

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

Jim Fan

165,246 Aufrufe • vor 1 Jahr

A lot of robot learning pipelines treat embodiment mismatch as a software problem. Collect data with one device, retarget the motion to another robot, and try to deal with the differences later. TwinDEX flips that idea around. This system is from a Chinese robotics company called X Square. Instead of starting with a generic data-collection device, the team co-designs a wearable three-finger end-effector and a robotic hand as two sides of the same learning system. They align the things that actually matter during manipulation: joint structure, link proportions, contact geometry, surface properties, visual appearance, sensing, and timing. That matters because dexterous manipulation is much more than matching hand poses. A few millimeters of contact error or a different force relationship can completely change how a task is executed. In the collection evaluation, TwinDEX delivered up to 5.3 times the effective throughput of on-robot teleoperation. It’s a useful reminder that scaling robot learning may not only be about bigger models and more data. Sometimes the hardware collecting the data is part of the data problem itself.

A lot of robot learning pipelines treat embodiment mismatch as a software problem. Collect data with one device, retarget the motion to another robot, and try to deal with the differences later. TwinDEX flips that idea around. This system is from a Chinese robotics company called X Square. Instead of starting with a generic data-collection device, the team co-designs a wearable three-finger end-effector and a robotic hand as two sides of the same learning system. They align the things that actually matter during manipulation: joint structure, link proportions, contact geometry, surface properties, visual appearance, sensing, and timing. That matters because dexterous manipulation is much more than matching hand poses. A few millimeters of contact error or a different force relationship can completely change how a task is executed. In the collection evaluation, TwinDEX delivered up to 5.3 times the effective throughput of on-robot teleoperation. It’s a useful reminder that scaling robot learning may not only be about bigger models and more data. Sometimes the hardware collecting the data is part of the data problem itself.

Alamin

48,112 Aufrufe • vor 11 Tagen

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 Aufrufe • vor 6 Monaten

“Do you know where a sidewalk begins and ends?” — a simple question from Niantic Spatial CEO, Inhi Cho Suh, that gets to the heart of what today’s AI is missing. For robots, this isn’t theoretical. To safely navigate something as everyday as a sidewalk, they need to understand boundaries, context, and change in real time. Learn about how Niantic Spatial is building the missing piece in Physical AI. #NianticSpatial #HumanX #GeospatialAI #Robotics #AI #PhysicalAI

“Do you know where a sidewalk begins and ends?” — a simple question from Niantic Spatial CEO, Inhi Cho Suh, that gets to the heart of what today’s AI is missing. For robots, this isn’t theoretical. To safely navigate something as everyday as a sidewalk, they need to understand boundaries, context, and change in real time. Learn about how Niantic Spatial is building the missing piece in Physical AI. #NianticSpatial #HumanX #GeospatialAI #Robotics #AI #PhysicalAI

Niantic Spatial 🌎

18,656 Aufrufe • vor 5 Monaten

𝗛𝘂𝗺𝘆𝗻𝗹𝗮𝗯𝘀 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜. They collect thousands of hours of real human activities like what people see, hear, move, and feel and turn it into clean training data for robots. Unlike language models that learn from the internet, robots need real world experience for messy everyday tasks such as ironing while a child runs past, restocking shelves in a crowded store, or assembling parts on a busy factory floor. Humyn Labs' approach uses four sensory streams captured in actual environments so robots can learn practical skills from human experience.

𝗛𝘂𝗺𝘆𝗻𝗹𝗮𝗯𝘀 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜. They collect thousands of hours of real human activities like what people see, hear, move, and feel and turn it into clean training data for robots. Unlike language models that learn from the internet, robots need real world experience for messy everyday tasks such as ironing while a child runs past, restocking shelves in a crowded store, or assembling parts on a busy factory floor. Humyn Labs' approach uses four sensory streams captured in actual environments so robots can learn practical skills from human experience.

𝐓𝐡𝐞𝐎𝐧𝐥𝐲𝟏𝐇𝐞𝐫𝐨👑

18,514 Aufrufe • vor 19 Tagen

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

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

111,276 Aufrufe • vor 1 Monat