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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... at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.show more

RoboPapers

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85,927 görüntüleme • 11 ay önce •via X (Twitter)

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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..

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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Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

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RoboPapers

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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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MTS

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Spatial understanding is important to moving around in complex environments and is a huge part of the challenge of generalizing to new scenes. Most world models, however, largely ignore this spatial dimension, focusing on 2D images. Not PointWorld, though. PointWorld is a 3D world model trained from real and simulated data which can perform a wide variety of manipulation tasks on a real robot, including grasping or handling articulated objects, all without any additional fine tuning. Wenlong Huang joins us to tell us more about what makes this work and how it’s different from other world models. Watch Episode #83 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

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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!

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RoboPapers

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RoboPapers

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Excitement about world-action models and robot learning has never been higher — they promise a way to use human egocentric data to train massive robotics models which can provide the “GPT” moment for robotics and unlock general-purpose embodied intelligence. And yet there’s been little concrete demonstration of scaling in robot learning. Dyna Robotics aims to change that, with an in-depth look at how scaling works as they approach 1 million hours of training data. Jason Ma Tianyu Li Anh-Quan Pham and Chet Bhateja joined us to tell us more. They show that as the amount of data they use in pretraining increased, they saw predictable, statistically significant gains on accuracy metrics on held-out data (data not seen during training). They go on to talk about what they learned, and show how this can be applied to many different problems. Watch Episode #99 of RoboPapers, with Michael Cho - Rbt/Acc Chris Paxton Jiafei Duan✈️CoRL2026 today to learn more!

Excitement about world-action models and robot learning has never been higher — they promise a way to use human egocentric data to train massive robotics models which can provide the “GPT” moment for robotics and unlock general-purpose embodied intelligence. And yet there’s been little concrete demonstration of scaling in robot learning. Dyna Robotics aims to change that, with an in-depth look at how scaling works as they approach 1 million hours of training data. Jason Ma Tianyu Li Anh-Quan Pham and Chet Bhateja joined us to tell us more. They show that as the amount of data they use in pretraining increased, they saw predictable, statistically significant gains on accuracy metrics on held-out data (data not seen during training). They go on to talk about what they learned, and show how this can be applied to many different problems. Watch Episode #99 of RoboPapers, with Michael Cho - Rbt/Acc Chris Paxton Jiafei Duan✈️CoRL2026 today to learn more!

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)

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)

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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

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

11,791 görüntüleme • 3 ay 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 ->

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

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Yann LeCun (Chief AI Scientist, Meta, Yann LeCun), Pim de Witte (CEO, General Intuition), and Aude Durand (Kyutai, Aude Durand), talk about world models, embodied agents, Yann's new company, and the limitations of LLMs 0:00 - Introduction to World Models 5:00 - Why World Models, Intuition & Introducing Yann's new company 10:00 - Architectures + Merging Language & Interaction Data towards General Agents 20:00 - Open Source, Sovereign AI & kyutai Partnership Keynote for #aiPULSE2025 at Station F in Paris 🇫🇷

Yann LeCun (Chief AI Scientist, Meta, Yann LeCun), Pim de Witte (CEO, General Intuition), and Aude Durand (Kyutai, Aude Durand), talk about world models, embodied agents, Yann's new company, and the limitations of LLMs 0:00 - Introduction to World Models 5:00 - Why World Models, Intuition & Introducing Yann's new company 10:00 - Architectures + Merging Language & Interaction Data towards General Agents 20:00 - Open Source, Sovereign AI & kyutai Partnership Keynote for #aiPULSE2025 at Station F in Paris 🇫🇷

General Intuition

112,171 görüntüleme • 11 ay önce

.General Intuition CEO Pim de Witte discusses using data from gamers to train AI & broader implications for the labor market:

.General Intuition CEO Pim de Witte discusses using data from gamers to train AI & broader implications for the labor market:

Squawk Box

47,043 görüntüleme • 4 ay önce

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!

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 görüntüleme • 7 ay önce

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."

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

80,994 görüntüleme • 4 ay önce

This is a good explanation of what we do at General Intuition, and why it’ll be a big unlock. We train general agents who are great at pixels to actions, partially using world models as a way to scale data and capabilities. If you’re interested in joining us, reach out!

This is a good explanation of what we do at General Intuition, and why it’ll be a big unlock. We train general agents who are great at pixels to actions, partially using world models as a way to scale data and capabilities. If you’re interested in joining us, reach out!

Pim de Witte

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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!

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

30,095 görüntüleme • 5 ay önce

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!

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

28,039 görüntüleme • 9 ay önce