Surgical robot simulation platform plays a crucial role in enhancing training efficiency and advancing research on robot learning. Much effort have been made by scholars on developing open-sourced surgical robot simulators to facilitate research. We also developed SurRoL formerly, an open-source, da Vinci Research Kit (dVRK) compatible and interactive embodied environment for robot learning. Despite its advancements, the simulation of soft bodies still remained a major challenge within the open-source platforms available for surgical robotics. To this end, we develop an interactive physically based soft body simulation framework and integrate it to SurRoL. Specifically, we utilized a high-performance adaptation of the Material Point Method (MPM) along with the Neo-Hookean model to represent the deformable tissue. Lagrangian particles are used to track the motion and deformation of the soft body throughout the simulation and Eulerian grids are leveraged to discretize space and facilitate the calculation of forces, velocities, and other physical quantities. We also employed an efficient collision detection and handling strategy to simulate the interaction between soft body and rigid tool of the surgical robot. By employing the Taichi programming language, our implementation harnesses parallel computing to boost simulation speed. Experimental results show that our platform is able to simulate soft bodies efficiently with strong physical interpretability and plausible visual effects. These new features in SurRoL enable the efficient simulation of surgical tasks involving soft tissue manipulation and pave the path for further investigation of surgical robot learning. The code will be released in a new branch of SurRoL github repo.

翻译：手术机器人仿真平台在提升训练效率和推进机器人学习研究中发挥着关键作用。学者们为开发开源手术机器人仿真器以促进研究付出了诸多努力。我们此前也开发了SurRoL——一个开源的、兼容达芬奇研究套件（dVRK）且支持交互的具身环境，用于机器人学习。尽管取得进展，但在面向手术机器人的开源平台中，软组织仿真仍是一大主要挑战。为此，我们开发了一个交互式的基于物理的软组织仿真框架并将其集成至SurRoL。具体而言，我们采用材料点法（MPM）的高性能适配版本结合Neo-Hookean模型来表征可变形组织。利用拉格朗日粒子追踪软组织在仿真全程中的运动与变形，并通过欧拉网格离散化空间，以辅助力、速度等物理量的计算。我们还采用高效碰撞检测与处理策略来模拟软组织与手术机器人刚性工具之间的交互。通过运用Taichi编程语言，我们的实现利用并行计算提升了仿真速度。实验结果表明，该平台能够高效仿真软组织，具备强物理可解释性与逼真的视觉效果。SurRoL的这些新特性可实现涉及软组织操控的外科任务的高效仿真，为手术机器人学习的进一步研究铺平道路。代码将发布在SurRoL的GitHub仓库新分支中。