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For general-purpose robots to be useful, we must be able to teach them new tasks quickly and repurpose them to new roles. But most techniques for teaching robots — even from human demonstrations — take a great deal of new data. But it turns out that it is possible,...

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RoboPapers

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19,495 просмотров • 1 месяц назад •via X (Twitter)

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Фото профиля Tony Tong | Founder | Ancient Systems x AI
Tony Tong | Founder | Ancient Systems x AI1 месяц назад

Perfect demos are a trap. They prove you can perform a behavior, not learn from one. Robot learning fails when we treat it as deterministic. The systems I work with are probabilistic: a mirror, not a crystal ball. Fast learning means updating on every signal, not locking in.

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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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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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24,677 просмотров • 4 месяцев назад

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27,904 просмотров • 7 месяцев назад

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27,360 просмотров • 5 месяцев назад

Teaching robots from human video is an important part of overcoming the “data gap” in robotics, but many of the details still need to be worked out. Homanga Bharadhwaj tells us about two recent research papers, Gen2Act and Spider, which go over different aspects of the problem: Gen2Act uses generative video models to create a reference of how a task should be performed given a language prompt; then, it uses a multi-purpose policy that can “translate” from human video to robot motion. However, Gen2Act has its limitations, in particular when it comes to dexterous, contact-rich tasks. That’s where SPIDER comes in: it uses human data together with simulation to train policies across many different humanoid hands and datasets. Also of note is that this is our first episode with our new rotating co-host, Jiafei Duan. To learn more, watch Episode #57 of RoboPapers now, with Chris Paxton and Jiafei Duan!
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24,286 просмотров • 8 месяцев назад

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26,544 просмотров • 1 месяц назад

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30,078 просмотров • 4 месяцев назад

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24,802 просмотров • 5 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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44,824 просмотров • 9 месяцев назад

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20,434 просмотров • 9 месяцев назад

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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 • 3 месяцев назад

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!
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21,434 просмотров • 7 месяцев назад

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18,813 просмотров • 8 месяцев назад

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 about how to achieve this fundamental capability for long-horizon task execution. Watch Episode #54 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton to learn more!
50:41

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17,875 просмотров • 9 месяцев назад

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23,905 просмотров • 6 месяцев назад

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RoboPapers

48,645 просмотров • 4 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

14,110 просмотров • 5 дней назад