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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,... show more

RoboPapers

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28,604 просмотров • 1 месяц назад •via X (Twitter)

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$Фото профиля Joseph Sottile 🌊 | Diffraction$

Joseph Sottile 🌊 | Diffraction1 месяц назад

This is awesome! Is there a specific type of video capture for training that you need to help push research further?

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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!

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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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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

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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 • 8 месяцев назад

Teleoperating a robot is hard. This means that when performing a robot task via teleoperation — say, to collect examples for training a robot policy — it’s almost unavoidably slower than you would like, below either the capabilities of the human expert on their own or the robot performing the task. Wouldn’t it be great if there was a way to fix this? Unfortunately, it’s harder than it looks. You can’t just execute faster, as this alters the distribution of environment states the policy will encounter. Nadun Ranawaka Arachchige and Zhenyang Chen propose Speed-Adaptive Imitation Learning (SAIL), which adds error-adaptive guidance, adapts execution speed according to task structure, predicts controller-invariant action targets to ensure robustness across execution speeds, and explicitly models delays from, for example, sensor latency. Watch episode #59 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton to learn more!

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World models — action-conditioned predictive models of the environment — are an exciting are of research for robots that can be useful both for training and for test-time compute. But video-based world models waste a lot of predictive power on reconstructing pixels, which makes model and data requirements much higher and limits how far out into the future their predictions remain viable. Instead, what if we learned a purely semantic world model, one which predicts which properties will be true about the world after a sequence of actions, without reconstructing the whole images? Jacob Berg tells us more. Watch Episode #53 of RoboPapers now, with Michael Cho - Rbt/Acc and Chris Paxton!

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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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27,360 просмотров • 5 месяцев назад

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RoboPapers

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38,772 просмотров • 3 месяцев назад

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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30,095 просмотров • 4 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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20,434 просмотров • 9 месяцев назад

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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RoboPapers

11,399 просмотров • 10 месяцев назад

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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RoboPapers

44,825 просмотров • 10 месяцев назад

One of the key advantages of legged robots like humanoids should be how effectively they can move across a wide variety of terrain types to accomplish their task. But Light-Loco-Parkour from the team at Light Origins aims to change that: using only onboard sensing, they show a policy which can decide when to walk, vault, climb, or otherwise traverse as it moves through a complex environment. Unlike many others, it uses sparse seeds instead of relying on a large motion corpus, learning when to use its skills to move around without specific sub-task labels. xiaodao chen and Yuntao Ma join us to go into the details. Watch Episode 104 of RoboPapers now, with Michael Cho - Rbt/Acc and Chris Paxton, to learn more!

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RoboPapers

14,631 просмотров • 16 дней назад