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We can learn a model that provides shaped "process rewards" for robotic RL, that evolves automatically as the policy gets better. This improves performance on benchmarks, and works in the real world! Some fun new work with Raymond Tsao & Andrew Wagenmaker

Sergey Levine

140,164 subscribers

38,514 次观看 • 4 个月前 •via X (Twitter)

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Sergey Levine 的头像

Sergey Levine4 个月前

In the real world, this leads to drastically faster learning, in just a few thousand time steps.

Sergey Levine 的头像

Sergey Levine4 个月前

To learn more, check out website: paper:

Hari 的头像

Hari4 个月前

@ajwagenmaker This is cool! Every time I do robotics research I remember dense reward is still overpowered for task mastery, and the more self supervised we can learn the better :)

Manan 的头像

Manan4 个月前

@ajwagenmaker Cool!

Ferbin 的头像

Ferbin4 个月前

@ajwagenmaker Does that hold with actual robots, or just in simulation?

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