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AdaDex: Learning Generalizable Dexterous Skills from a Single Trajectory via Hybrid Dexterous Mimicking

AdaDex is a goal-conditioned reinforcement learning framework that turns a few seconds of human hand-object trajectory (from motion capture data or HOI reconstruction from in-the-wild RGB(D) video) into generalizable dexterous skills. Its core, Hybrid Dexterous Mimicking, trains a shared policy across two sets of massively parallel environments: environments with dense motion tracking objectives inject the human prior, while environments with noised initial states and goals and a sparse object-success reward force the skill to work even when it deviates from the reference trajectory.

Part of the event: AdaDex: Training Generalizable Dexterous Skills from a Single Human Hand Trajectory

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