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ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
The paper introduces ADEPT, a reinforcement learning framework for training multi-fingered robots to perform complex tasks directly from raw visual and tactile perception. The framework involves pre-training a…
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Researchers introduce REGRIND, a method that enables robots to learn dexterous manipulation policies from a single human demonstration. The method retargets hand-object motion to a robot reference and trains an RL…
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