Pre-training on a generic manipulation task and using a stable fine-tuning recipe with behavior cloning and conservative updates enables efficient transfer of dexterous skills to new tasks and real robots without catastrophic forgetting.
ADEPT is a reinforcement learning framework that trains dexterous robot hands to perform complex manipulation tasks by first learning a general object-handling skill, then adapting it to specific downstream tasks. The system transfers from simulation to real robots with multi-fingered hands (23-29 degrees of freedom) using vision and touch sensors, solving long-horizon tasks at human-level speed.