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With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team...

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General Intuition CEO Pim de Witte (Pim de Witte) says: "Most of AI is just glorified data science." And... "Yann LeCun is underrated." His reasoning? "Good models mostly are downstream from good data & simple things." General Intuition is taking a different approach than traditional LLMs: "We train models that can predict lots of different actions, where text is just a subset." When I asked why he thinks Yann LeCun is underrated..
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SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.
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SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

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RoboPapers

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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!
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RoboPapers

32,717 Aufrufe • vor 5 Tagen

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Collecting robot data at scale is key to deploying working manipulation policies, and the team from Tutor Intelligence Tutor is here to tell us about how to accomplish it. Their new announcement: a massive, 100-robot “data factory,” with a behind-the-scenes look at how to build a teleoperation platform and how to make robots and policies that are useful for their customers. Tutor Intelligence is a full-stack robotics company: they build robot arms, they sell robot arms, they write the software and they train neural networks. Josh Gruenstein, Jesse Michel, Shiraz, and Joe McCalmon, and Joe McCalmon join us to tell us more about how they scale both teleop data and human interventions from their teleoperators in order to train the policies they need. Watch Episode #85 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

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Pretraining is essential for good performance on a wide variety of robotics tasks, and so most vision-language-action models build off of a vision language model (VLM) trained on a wide variety of image-language data. But how does the choice of VLM translate to downstream robotics performance? Jianke Zhang and @GYanjiang join us to talk about this key part of the robot policy, looking at a wide variety of different VLMs and how they perform. Interestingly, they see that performance on auxiliary tasks like quesiton answering did not lead to downstream improvements in control. To learn more, watch episode 65 of RoboPapers now, with Chris Paxton and Jiafei Duan!

RoboPapers

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Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Abhishek Gupta

11,994 Aufrufe • vor 1 Jahr

General Intuition CEO Pim de Witte says Twitch and YouTube can't touch his dataset because neither of them runs natively on your machine: "The same way that DeepMind has YouTube, right? And their models are increasingly good at spatial temporal video-related tasks. We have Medal, which is the only network that has this action data." "If you're Twitch, you don't run natively, so you don't have this data. If you're YouTube, you don't run natively, so you don't have this data." "And so as a result, we're the only company that can take that same large-scale network plus frontier lab bet on the action predictions." "It's the same thesis, right? DeepMind and YouTube do it for video prediction. Reddit and ChatGPT, in many ways, did it for their initial sets of text, and we're doing it for action prediction." Pim de Witte General Intuition
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General Intuition CEO Pim de Witte says Twitch and YouTube can't touch his dataset because neither of them runs natively on your machine: "The same way that DeepMind has YouTube, right? And their models are increasingly good at spatial temporal video-related tasks. We have Medal, which is the only network that has this action data." "If you're Twitch, you don't run natively, so you don't have this data. If you're YouTube, you don't run natively, so you don't have this data." "And so as a result, we're the only company that can take that same large-scale network plus frontier lab bet on the action predictions." "It's the same thesis, right? DeepMind and YouTube do it for video prediction. Reddit and ChatGPT, in many ways, did it for their initial sets of text, and we're doing it for action prediction." Pim de Witte General Intuition

MTS

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General Intuition

111,795 Aufrufe • vor 9 Monaten

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.
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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.

ABBA

90,854 Aufrufe • vor 2 Tagen

Robots, unfortunately, tend to be expensive. And finding a robot that’s both capable of performing a wide variety of mobile manipulation tasks, and is affordable and “hackable”, is extremely difficult. Many different problems need to be addressed, from arm control to navigation to integrating your data collection strategy into hardware design. This can make it difficult for all but the most well-funded teams to “scale” real-world robotics research. Fortunately, the team behind Build Your Own Robot has a solution. Manan Anjaria, Mahi Shafiullah 🏠🤖,Jeff Cui, and Enes Erciyes joined us to talk about how they build a fully open-source mobile manipulator out of off-the-shelf parts, which has humanlike range of motion, and can perform a wide variety of tasks, all while being only roughly $10,000 to build. Watch Episode 71 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, today to learn more!
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Robots, unfortunately, tend to be expensive. And finding a robot that’s both capable of performing a wide variety of mobile manipulation tasks, and is affordable and “hackable”, is extremely difficult. Many different problems need to be addressed, from arm control to navigation to integrating your data collection strategy into hardware design. This can make it difficult for all but the most well-funded teams to “scale” real-world robotics research. Fortunately, the team behind Build Your Own Robot has a solution. Manan Anjaria, Mahi Shafiullah 🏠🤖,Jeff Cui, and Enes Erciyes joined us to talk about how they build a fully open-source mobile manipulator out of off-the-shelf parts, which has humanlike range of motion, and can perform a wide variety of tasks, all while being only roughly $10,000 to build. Watch Episode 71 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, today to learn more!

RoboPapers

34,151 Aufrufe • vor 5 Monaten

General Intuition CEO Pim de Witte, who's building foundation models trained on video game controller input data ("action-labeled gameplay clips"), says general intelligence won't "taste like an LLM": "We have a scale of data that's going to allow us to jump to the frontier in one capability — which is any system that can be controlled with a game controller (which is most robots) — and then, you can use that to create a sufficiently general intelligence." "As humans, the decision to talk or type is a very, very small subset of the actions that we can actually take." "So in order to create a sufficiently general intelligence to play 10,000+ video games, the model has to be able to predict across the entire action space of human cognition when they're interacting with these environments. Which are 2D and 3D environments, interfaces, long-horizon tasks, short-horizon tasks, [etc.]." "It has to be a sufficiently general intelligence in order to predict actions. Therefore, the type of model you get out is not going to taste like an LLM. This model is going to be incredibly good at navigating unforeseen environments. It's going to be incredibly good at zero-shotting any task that can be done with a game controller."
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General Intuition CEO Pim de Witte, who's building foundation models trained on video game controller input data ("action-labeled gameplay clips"), says general intelligence won't "taste like an LLM": "We have a scale of data that's going to allow us to jump to the frontier in one capability — which is any system that can be controlled with a game controller (which is most robots) — and then, you can use that to create a sufficiently general intelligence." "As humans, the decision to talk or type is a very, very small subset of the actions that we can actually take." "So in order to create a sufficiently general intelligence to play 10,000+ video games, the model has to be able to predict across the entire action space of human cognition when they're interacting with these environments. Which are 2D and 3D environments, interfaces, long-horizon tasks, short-horizon tasks, [etc.]." "It has to be a sufficiently general intelligence in order to predict actions. Therefore, the type of model you get out is not going to taste like an LLM. This model is going to be incredibly good at navigating unforeseen environments. It's going to be incredibly good at zero-shotting any task that can be done with a game controller."

TBPN

80,883 Aufrufe • vor 2 Monaten

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian ✈️ IROS join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!
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Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian ✈️ IROS join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

RoboPapers

30,078 Aufrufe • vor 4 Monaten

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!
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Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

RoboPapers

27,904 Aufrufe • vor 7 Monaten

Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?
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Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?

Bri Teresi

25,101 Aufrufe • vor 5 Monaten

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and Kingston Kuan spent years building large-scale infrastructure at Jane Street.
2:17

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and Kingston Kuan spent years building large-scale infrastructure at Jane Street.

Brandon Ong

166,522 Aufrufe • vor 1 Monat

Data for the Real World Diana & Austin Tindle AI has become superhuman at code, language, and images because it has enormous amounts of data to learn from. But our understanding of the physical world is still limited by sparse sensors and models built largely on intuition. Cheaper sensors and better foundation models could unlock entirely new datasets for energy, agriculture, logistics, construction, and the climate. Once you can accurately model the physical world, you can begin to control it.
1:05

Data for the Real World Diana & Austin Tindle AI has become superhuman at code, language, and images because it has enormous amounts of data to learn from. But our understanding of the physical world is still limited by sparse sensors and models built largely on intuition. Cheaper sensors and better foundation models could unlock entirely new datasets for energy, agriculture, logistics, construction, and the climate. Once you can accurately model the physical world, you can begin to control it.

Y Combinator

23,521 Aufrufe • vor 1 Monat