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Robotics fundamentally involves understanding the dynamics of how things change in the world in response to action and force. This is impossible to learn from static images; instead, it’s far more effective and more data-efficient to learn from video. Elvis Nava joins us to talk about @mimicrobotic. One of... show more

RoboPapers

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48,892 次观看 • 5 个月前 •via X (Twitter)

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Elvis Nava5 个月前

Loved to chat!

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Lukas Die Kunst5 个月前

@elvisnavah in my UAV work temporal context is everything. snapshots distort aerodynamic reality

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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!

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!

RoboPapers

24,734 次观看 • 6 个月前

Robots has a data problem, in that robotics data is rare. While human video is quite common, it’s not usually directly usable for robots for a variety of reasons, most significantly that it’s missing explicit, accurate robot actions. Instead, Jeremy Collins proposes that we predict keypoint trajectories — basically, how any given point in an object will move as a robot performs a task. This lets us use action-free human video to train robot skills. Learn more by watching Episode #37 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now.

Robots has a data problem, in that robotics data is rare. While human video is quite common, it’s not usually directly usable for robots for a variety of reasons, most significantly that it’s missing explicit, accurate robot actions. Instead, Jeremy Collins proposes that we predict keypoint trajectories — basically, how any given point in an object will move as a robot performs a task. This lets us use action-free human video to train robot skills. Learn more by watching Episode #37 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now.

RoboPapers

89,011 次观看 • 1 年前

How can we build robotic hands with truly superhuman dexterity? Daxo Robotics is developing a unique tendon-driven soft robot hand, which aims to be tougher and more capable than a traditional humanoid hand. Each finger consists of many different tendons, which act in concert to move or manipulate. This is a special episode of RoboPapers where, instead of talking about a scientific paper, we talk to Tom Zhang, founder of Daxo Robotics, in order to learn both about his background and about how this one-of-a-kind robot hand design works. Watch Episode #40 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now!

How can we build robotic hands with truly superhuman dexterity? Daxo Robotics is developing a unique tendon-driven soft robot hand, which aims to be tougher and more capable than a traditional humanoid hand. Each finger consists of many different tendons, which act in concert to move or manipulate. This is a special episode of RoboPapers where, instead of talking about a scientific paper, we talk to Tom Zhang, founder of Daxo Robotics, in order to learn both about his background and about how this one-of-a-kind robot hand design works. Watch Episode #40 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now!

RoboPapers

17,052 次观看 • 1 年前

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!

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

23,905 次观看 • 8 个月前

Reasoning over long horizons would allow robots to generalize better to unseen environments and settings zero-shot. One mechanism for this kind of reasoning would be world models, but traditional video world models still tend to struggle with long horizons, and are very data intensive to train. But what if instead of predicting images about the future, we predicted just the symbolic information necessary for reasoning? Nishanth Kumar tells us about Pixels to Predicates, a method for symbol grounding which allows a VLM to plan sequences of robot skills to achieve unseen goals in previously unseen settings. To find out more, watch episode #44 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now!

Reasoning over long horizons would allow robots to generalize better to unseen environments and settings zero-shot. One mechanism for this kind of reasoning would be world models, but traditional video world models still tend to struggle with long horizons, and are very data intensive to train. But what if instead of predicting images about the future, we predicted just the symbolic information necessary for reasoning? Nishanth Kumar tells us about Pixels to Predicates, a method for symbol grounding which allows a VLM to plan sequences of robot skills to achieve unseen goals in previously unseen settings. To find out more, watch episode #44 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now!

RoboPapers

19,749 次观看 • 11 个月前

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

26,279 次观看 • 2 个月前

The holy grail of robotics is to be able to perform previously-unseen, out-of-distribution manipulation tasks “zero shot” in a new environment. NovaFlow proposes an approach which (1) generates a video, (2) computes predicted flow — how points move through the scene — and (3) uses this flow as an objective to generate a motion. Using this procedure, NovaFlow generates motions in unseen scenes, for unseen tasks, and can transfer across embodiments. To learn more, we are joined by Hongyu Li and Jiahui Fu from RAI. Watch Episode #63 of RoboPapers with Chris Paxton and Michael Cho - Rbt/Acc now to learn more!

The holy grail of robotics is to be able to perform previously-unseen, out-of-distribution manipulation tasks “zero shot” in a new environment. NovaFlow proposes an approach which (1) generates a video, (2) computes predicted flow — how points move through the scene — and (3) uses this flow as an objective to generate a motion. Using this procedure, NovaFlow generates motions in unseen scenes, for unseen tasks, and can transfer across embodiments. To learn more, we are joined by Hongyu Li and Jiahui Fu from RAI. Watch Episode #63 of RoboPapers with Chris Paxton and Michael Cho - Rbt/Acc now to learn more!

RoboPapers

21,543 次观看 • 8 个月前

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this? Baoxiong Jia tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP. To learn more, watch Episode #49 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton.

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this? Baoxiong Jia tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP. To learn more, watch Episode #49 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton.

RoboPapers

44,826 次观看 • 11 个月前

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!

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

16,197 次观看 • 5 个月前

Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also generalizes across objects, tasks, and scenes. Jiangran Lyu joins us to explain. Learn more on Episode 98 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton!

Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also generalizes across objects, tasks, and scenes. Jiangran Lyu joins us to explain. Learn more on Episode 98 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton!

RoboPapers

16,345 次观看 • 2 个月前

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!

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

36,041 次观看 • 5 个月前

Memory is one of the most important problems in robotics. Long horizon memory is key for a variety of robot manipulation problems. However, there exist no good benchmarks for understanding progress in how well generalist robot policies can understand language. Yinpei Dai and Yuejiang Liu made RoboMME as a solution: it’s a large benchmark which shows 16 different robot tasks, like counting objects or mastering timing. They show 14 different memory-augmented generalist policies across these different benchmarks. It’s an incredibly thorough and interesting result, aimed at driving forward this core robotic capability. To learn more, watch Episode 91 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton!

Memory is one of the most important problems in robotics. Long horizon memory is key for a variety of robot manipulation problems. However, there exist no good benchmarks for understanding progress in how well generalist robot policies can understand language. Yinpei Dai and Yuejiang Liu made RoboMME as a solution: it’s a large benchmark which shows 16 different robot tasks, like counting objects or mastering timing. They show 14 different memory-augmented generalist policies across these different benchmarks. It’s an incredibly thorough and interesting result, aimed at driving forward this core robotic capability. To learn more, watch Episode 91 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton!

RoboPapers

30,785 次观看 • 3 个月前

Evaluating robot policies is hard. Every lab has a different robot; reproducible evaluations are really challenging. This makes it hard for us to know which methods for learning robot policies are likely to perform the best in real-world scenarios. Taking a page from LLM evaluations like Chatbot Arena, RoboArena aims to address this problem through crowdsourcing evaluations with a network of different evaluators. Watch Episode #34 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton , now to learn more from authors Pranav Atreya and Karl Pertsch!

Evaluating robot policies is hard. Every lab has a different robot; reproducible evaluations are really challenging. This makes it hard for us to know which methods for learning robot policies are likely to perform the best in real-world scenarios. Taking a page from LLM evaluations like Chatbot Arena, RoboArena aims to address this problem through crowdsourcing evaluations with a network of different evaluators. Watch Episode #34 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton , now to learn more from authors Pranav Atreya and Karl Pertsch!

RoboPapers

15,056 次观看 • 1 年前

Reinforcement on robots is highly limited by our ability to design good reward functions; this means that designing strong, generalizable reward functions is a key enabler to progress on real-world reinforcement learning. But we already have a very general class of models: VLMs. Wouldn’t it be great if you could just use a VLM to generate rewards, then? TOPReward directly generates rewards from the probability of the “True” token of a VLM question-answering response; this makes it easy to implement, incredibly general, and surprisingly powerful. We talked to Shirui and Cole Harrison to learn more. Watch Episode#75 of RoboPapers now to learn more, with Chris Paxton and Jiafei Duan!

Reinforcement on robots is highly limited by our ability to design good reward functions; this means that designing strong, generalizable reward functions is a key enabler to progress on real-world reinforcement learning. But we already have a very general class of models: VLMs. Wouldn’t it be great if you could just use a VLM to generate rewards, then? TOPReward directly generates rewards from the probability of the “True” token of a VLM question-answering response; this makes it easy to implement, incredibly general, and surprisingly powerful. We talked to Shirui and Cole Harrison to learn more. Watch Episode#75 of RoboPapers now to learn more, with Chris Paxton and Jiafei Duan!

RoboPapers

13,185 次观看 • 6 个月前

Achieving human-like dexterity is the next frontier for robotics, and yet dexterity data is often subtly hard to scale. Real-world dexterity data, including things like finger-pose estimates, is often slightly off, making it physically invalid and hard to execute on real hardware and hard to learn from. DO AS I DO is an algorithm for reconstructing and retargeting monocular RGB videos to robot hands, outperforming the state of the art and even working from generated videos. Bhawna Paliwal Haritheja Will Liang and Mahi Shafiullah 🏠🤖 join us to tell us more. Watch Episode 107 of RoboPapers, with Chris Paxton and Jiafei Duan, now!

Achieving human-like dexterity is the next frontier for robotics, and yet dexterity data is often subtly hard to scale. Real-world dexterity data, including things like finger-pose estimates, is often slightly off, making it physically invalid and hard to execute on real hardware and hard to learn from. DO AS I DO is an algorithm for reconstructing and retargeting monocular RGB videos to robot hands, outperforming the state of the art and even working from generated videos. Bhawna Paliwal Haritheja Will Liang and Mahi Shafiullah 🏠🤖 join us to tell us more. Watch Episode 107 of RoboPapers, with Chris Paxton and Jiafei Duan, now!

RoboPapers

30,668 次观看 • 1 个月前

Just collecting manipulation data isn’t enough for robots - they need to be able to move around in the world, which has a whole different set of challenges from pure manipulation. And bringing navigation and manipulation together in a single framework is even more challenging. Enter HERMES, from Zhecheng Yuan and Tianming Wei. This is a four-stage process in which human videos are used to set up an RL sim-to-real training pipeline in order to overcome differences between robot and human kinematics, and used together with a navigation foundation model to move around in a variety of environments. To learn more, join us as Zhecheng Yuan and Tianming Wei tell us about how they built their system to perform mobile dexterous manipulation from human videos in a variety of environments. Watch Episode #45 of RoboPapers today, hosted by Michael Cho - Rbt/Acc and Chris Paxton!

Just collecting manipulation data isn’t enough for robots - they need to be able to move around in the world, which has a whole different set of challenges from pure manipulation. And bringing navigation and manipulation together in a single framework is even more challenging. Enter HERMES, from Zhecheng Yuan and Tianming Wei. This is a four-stage process in which human videos are used to set up an RL sim-to-real training pipeline in order to overcome differences between robot and human kinematics, and used together with a navigation foundation model to move around in a variety of environments. To learn more, join us as Zhecheng Yuan and Tianming Wei tell us about how they built their system to perform mobile dexterous manipulation from human videos in a variety of environments. Watch Episode #45 of RoboPapers today, hosted by Michael Cho - Rbt/Acc and Chris Paxton!

RoboPapers

15,092 次观看 • 11 个月前

Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand. In VideoManip, Hongyi Chen and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies. Watch episode #73 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, now to learn more!

Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand. In VideoManip, Hongyi Chen and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies. Watch episode #73 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, now to learn more!

RoboPapers

27,360 次观看 • 7 个月前

For robots to be useful, they can’t just dance — they must be able to physically interact with the world around them. Unfortunately, the sorts of motion tracking policies you see performing dancing or martial arts are not really capable of the kind of precise, forceful interaction needed to perform useful interactions with the world. Siheng Zhao and Yanjie Ze join us to talk about ResMimic, their new paper which takes a general-purpose human motion-tracking policy and improves it with a residual policy to reliably interact with objects To learn more, watch Episode #47 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, today!

For robots to be useful, they can’t just dance — they must be able to physically interact with the world around them. Unfortunately, the sorts of motion tracking policies you see performing dancing or martial arts are not really capable of the kind of precise, forceful interaction needed to perform useful interactions with the world. Siheng Zhao and Yanjie Ze join us to talk about ResMimic, their new paper which takes a general-purpose human motion-tracking policy and improves it with a residual policy to reliably interact with objects To learn more, watch Episode #47 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, today!

RoboPapers

11,399 次观看 • 11 个月前

It’s long been a dream of roboticists to be able to teach a robot in simulation so as to skip the long and expensive process of collecting large amounts of real-world training data. However, building simulations for robot tasks is extremely hard. Ideally, we could go from real data to a useful simulation. This is exactly what Guangqi Jiang and his co-authors do. they use 3d Gaussian splatting to reconstructed scenes which let them create interactive environments that, when combined with a physcs engine, allow for training robot policies that show zero-shot sim-to-real transfer (i.e., using no real-world demonstrations). To learn more, watch Episode 56 of Robopapers with Michael Cho - Rbt/Acc and Chris Paxton now!

It’s long been a dream of roboticists to be able to teach a robot in simulation so as to skip the long and expensive process of collecting large amounts of real-world training data. However, building simulations for robot tasks is extremely hard. Ideally, we could go from real data to a useful simulation. This is exactly what Guangqi Jiang and his co-authors do. they use 3d Gaussian splatting to reconstructed scenes which let them create interactive environments that, when combined with a physcs engine, allow for training robot policies that show zero-shot sim-to-real transfer (i.e., using no real-world demonstrations). To learn more, watch Episode 56 of Robopapers with Michael Cho - Rbt/Acc and Chris Paxton now!

RoboPapers

20,434 次观看 • 10 个月前