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

ArXi:2604.19677v1 Announce Type: cross Reinforcement learning-based control policies have been frequently nstrated to be effective than analytical techniques for many manipulation tasks. Commonly, these methods learn neural control policies that predict end-effector pose changes directly from observed state information. For tasks like inserting delicate connectors which induce force constraints, pose-based policies have limited explicit control over force and rely on carefully tuned low-level controllers to avoid executing damaging actions.