Article

PlayPolicy: Learning Dexterous Manipulation from Supervised Play

The authors propose the PlayPolicy framework, in which a robot first learns a play policy from minutes of human play data through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model; at inference time, this policy proposes candidate action sequences for model-predictive control (MPC).

Part of the event: PlayPolicy: Learning Dexterous Manipulation from Human Play Data

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