mlsanityData to 14 Oct 2026

A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics

arXiv 2610.15422 ↗First posted 11 Oct 2026cs.RO cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 37 references that are in the map. First posted on arXiv 11 Oct 2026.

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7 of 37 references.

References · 7

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  4. 2021Co-GAIL: Learning Diverse Strategies for Human-Robot Collaborationcited by 17
  5. 2020Real-World Human-Robot Collaborative Reinforcement Learningcited by 9
  6. 2025Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARLcited by 6
  7. 2024Toward Adaptive Cooperation: Model-Based Shared Control Using LQ-Differential Gamescited by 1

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  1. 2026SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints
  2. 2023Design and Evaluation of an Uncertainty-Aware Shared-Autonomy System with Hierarchical Conservative Skill Inference
  3. 2025Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
  4. 2019Operation and Imitation under Safety-Aware Shared Control
  5. 2026Efficient and Reliable Teleoperation through Real-to-Sim-to-Real Shared Autonomy
  6. 2026Dual-Agent Multiple-Model Reinforcement Learning for Event-Triggered Human-Robot Co-Adaptation in Decoupled Task Spaces
  7. 2025Reinforcement Learning for Robotic Safe Control with Force Sensing
  8. 2025ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration