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Most robot policies today still largely lack memory: they make all their decisions based on what they can see right now. MemER aims to change that by learning which frames are important; this lets it deal with tasks like object search. Ajay Sridhar, Jenny Pan, and @satviks107Sharma tell us...

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RoboPapers

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17,875 просмотров • 8 месяцев назад •via X (Twitter)

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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!
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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!

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Learning policies via imitation is extremely potent, but making sure those policies will generalize to out of distribution settings is still very hard. SAILOR proposes a solution in learning to search via a learned world model, which outperforms existing imitation approaches. Gokul Swamy, Arnav Jain , and Vibhakar Mohta tell us about their approach. Watch Episode #33 of RoboPapers, co-hosted by Michael Cho - Rbt/Acc and Chris Paxton , now!
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Learning policies via imitation is extremely potent, but making sure those policies will generalize to out of distribution settings is still very hard. SAILOR proposes a solution in learning to search via a learned world model, which outperforms existing imitation approaches. Gokul Swamy, Arnav Jain , and Vibhakar Mohta tell us about their approach. Watch Episode #33 of RoboPapers, co-hosted by Michael Cho - Rbt/Acc and Chris Paxton , now!

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21,440 просмотров • 11 месяцев назад

We’ve all seen videos of humanoid robots performing single tasks that are very impressive, like dancing or karate. But training humanoid robots to perform a wide range of complex motions is difficult. GMT is a general-purpose policy which can learn a wide range of robot motions. Watch Episode #32 of RoboPapers, with Zixuan Chen , co-hosted by Michael Cho - Rbt/Acc and Chris Paxton , to learn more.
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We’ve all seen videos of humanoid robots performing single tasks that are very impressive, like dancing or karate. But training humanoid robots to perform a wide range of complex motions is difficult. GMT is a general-purpose policy which can learn a wide range of robot motions. Watch Episode #32 of RoboPapers, with Zixuan Chen , co-hosted by Michael Cho - Rbt/Acc and Chris Paxton , to learn more.

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16,605 просмотров • 11 месяцев назад

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!

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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.
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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.

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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!
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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!
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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!

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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!
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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!

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36,041 просмотров • 2 месяцев назад

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!
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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!

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In order for robots to be deployed in the real world, performing tasks of real value, they must be reliable. Unfortunately, even more, most robotic demos work maybe 70-80% of the time at best. The way to get better reliability is to do real-world reinforcement learning: having the robot teach itself how to perform the task up to a high level of success. The key to doing this is to start with a core of expert human data, use that to train a policy then iteratively improve it, until finally finishing with on-policy reinforcement learning. Kun Lei talks through a unified framework for imitation and reinforcement learning based on PPO, which enables this improvement process. In this episode, Kun Lei explains the theory behind his reinforcement learning method and how it allowed his robot to run in a shopping mall juicing oranges for seven hours at a time, among experiments on a wide variety of tasks and embodiments. Watch episode 58 of RoboPapers now, hosted by Michael Cho - Rbt/Acc and Chris Paxton!
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18,813 просмотров • 7 месяцев назад

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!
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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!

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Collecting dexterous humanoid robot data is difficult to scale. That's why Mengda Xu and Han Zhang built DexUMI: a tool for demonstrating how to control a dexterous robot hand, which allows you to quickly collect task data. Co-hosted by Michael Cho - Rbt/Acc and Chris Paxton
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Diffusion Policy was one of the big breakthroughs that has enabled an explosion in real-world robot learning. However, it’s always had a weakness, which is that it works by computing a final action trajectory from random noise, which leads to high latency when predicting a final action sequence. Instead, why not initialize the search based on previous actions? This allows for incredibly fast policy inference and in many cases improved generalization, generating high-quality predictions with sub-ms latency. Jindou Jia and Jianfei Yang join us to explain. Learn more on Episode 95 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton!
49:19

Diffusion Policy was one of the big breakthroughs that has enabled an explosion in real-world robot learning. However, it’s always had a weakness, which is that it works by computing a final action trajectory from random noise, which leads to high latency when predicting a final action sequence. Instead, why not initialize the search based on previous actions? This allows for incredibly fast policy inference and in many cases improved generalization, generating high-quality predictions with sub-ms latency. Jindou Jia and Jianfei Yang join us to explain. Learn more on Episode 95 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton!

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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.
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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.

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