Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

Researchers propose hybrid position-force control policies that let reinforcement learning agents dynamically switch between force and position control for delicate manipulation tasks like connector insertion. A new training method called MATCH improves learning efficiency by handling contact mode transitions.

MentionsMATCH · reinforcement learning

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