手術ロボティクスにおける安全意識型ハプティック共有制御のための強化学習フレームワーク
A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics
手術ロボットのハプティック共有制御を2エージェント協調マルコフゲームとしてモデル化し、MADDPGやrMAPPOなどのマルチエージェント強化学習手法を比較して、安全性とタスク性能のトレードオフを評価した。
著者: Zihao Zhang, Karl Handwerker, Balint Varga
分類: cs.RO, eess.SY
原文アブストラクト
Haptic shared control in surgical robotics combines human-side and robot-side forces at a shared end effector while preserving operator involvement. However, the effects of geometric safety choices on task execution and force coordination remain unclear. We model the interaction as a two-agent cooperative Markov game under centralized training and decentralized execution, comparing learned human control with a task-tuned fixed-impedance baseline. This model-free multi-agent reinforcement learning framework supports joint policy adaptation without requiring a complete analytical interaction model. It compares MADDPG and rMAPPO with an epigraph-constrained approach under common geometric task definitions. In simulation, rMAPPO generally achieved the highest return and tracking performance, whereas the epigraph approach produced more conservative motions and lower geometric exposure, including no observed collision or dangerous-region exposure after geometry transfer. With real operator input, fixed-impedance-trained policies were faster and successful in all recorded trials. Policies trained with a learned partner exhibited greater command opposition, while the fixed epigraph condition produced the most aligned commands. This work provides a benchmark for studying human-control representation and constraint-handling trade-offs in haptic shared control.