Keywords: self-play, asymmetric self-play, automatic curriculum, automatic goal generation, robotic learning, robotic manipulation, reinforcement learning
Abstract: We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. To do so, we rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game. Alice is asked to propose challenging goals and Bob aims to solve them. We show that this method is able to discover highly diverse and complex goals without any human priors. We further show that Bob can be trained with only sparse rewards, because the interaction between Alice and Bob results in a natural curriculum and Bob can learn from Alice's trajectory when relabeled as a goal-conditioned demonstration. Finally, we show that our method scales, resulting in a single policy that can transfer to many unseen hold-out tasks such as setting a table, stacking blocks, and solving simple puzzles. Videos of a learned policy is available at https://robotics-self-play.github.io.
One-sentence Summary: We use asymmetric self-play to train a goal-conditioned policy for complex object manipulation tasks, and the learned policy can zero-shot generalize to many manually designed holdout tasks.
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Reviewed Version (pdf): https://openreview.net/references/pdf?id=g9eNhiUYO