外观
论文arXiv 2610.1542217 天前
面向手术机器人的安全感知触觉共享控制强化学习框架
原文链接：A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics（arXiv 2610.15422 · 发布日期：2026-10-11）
A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics
针对手术机器人触觉共享控制中的安全几何影响，提出多智能体强化学习框架，并在仿真与真实操作者输入中验证。
核心要点
- 触觉共享控制需融合人侧与机器人侧作用力，但几何安全选择对任务执行和力协调的影响尚不清楚。
- 将交互建模为集中训练、分散执行的双智能体合作 Markov 博弈，支持无完整解析模型的联合策略自适应。
- 仿真中 rMAPPO 通常获得最高回报和跟踪性能，epigraph 方法动作更保守且几何暴露更低。
- 真实操作者输入下，固定阻抗训练策略更快且记录试验均成功，固定 epigraph 条件的命令最一致。
验证范围与局限：验证范围包括仿真和真实操作者输入下的共同几何任务定义。
英文摘要 (Abstract)
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.
作者：Zihao Zhang, Karl Handwerker, Bálint Varga
来源
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