Sampling-based planning used for reset states and pre-training makes reinforcement learning practical for dexterous in-hand manipulation of hard objects with intrinsic sensing.
Optimalityandapproximationwith policy gradient methods in markov decision processes,
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Towards Human-level Dexterity via Robot Learning
Sampling-based planning used for reset states and pre-training makes reinforcement learning practical for dexterous in-hand manipulation of hard objects with intrinsic sensing.