SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning
复制标题
SurRoL：一个以开源强化学习为中心且兼容 dVRK 的手术机器人学习平台
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
P. Heng
中科院分区：
文献类型：
--
作者：
Jiaqi Xu;Bin Li;Bo Lu;Yunhui Liu;Q. Dou;P. Heng
自主手术执行减轻了繁琐的程序和外科医生的疲劳。最近的基于学习的方法，特别是基于强化学习（RL）的方法，实现了灵巧操作，这通常需要仿真，以有效地收集数据，并降低硬件成本有前途的性能。现有的基于学习的医疗机器人仿真平台受限于有限的场景和简化的物理交互，这降低了学习策略的真实世界性能。在这项工作中，我们设计了SurRoL，RL为中心的仿真平台，手术机器人学习兼容的达芬奇研究工具包（dVRK）。所设计的SurRoL集成了一个用户友好的用于算法开发的RL库和一个实时物理引擎，能够支持更多的PSM/ECM场景和更真实的物理交互。该平台内置了10个基于学习的手术任务，这些任务在真实的自主手术执行中很常见。我们在仿真中使用RL算法评估SurRoL，提供深入的分析，在真实的dVRK上部署训练策略，并表明我们的SurRoL在真实的世界中实现了更好的可移植性。
Autonomous surgical execution relieves tedious routines and surgeon’s fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promising performance for dexterous manipulation, which usually requires the simulation to collect data efficiently and reduce the hardware cost. The existing learning-based simulation platforms for medical robots suffer from limited scenarios and simplified physical interactions, which degrades the real-world performance of learned policies. In this work, we designed SurRoL, an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK). The designed SurRoL integrates a user-friendly RL library for algorithm development and a real-time physics engine, which is able to support more PSM/ECM scenarios and more realistic physical interactions. Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.
影响因子：
5.2
作者：
James, Stephen;Ma, Zicong;Davison, Andrew J.
通讯作者：
Davison, Andrew J.