用物理引擎模拟手术缝合，让机器人学会精准穿针引线。
Simulation of Surgical Suturing Using Position-Based Dynamics and the Material Point Method for Robot Reinforcement Learning
- 结合位置基础动力学与物质点法，实现缝线与软组织的逼真交互。
- 在严格距离阈值下，穿针和拔针成功率分别达80%和68%。
- 专为强化学习设计，适合研究医疗机器人自主操作的学者。
近年来机器人研究的进展推动了高性能模拟器的需求。外科机器人模拟面临独特挑战，需建模刚性器械、柔软组织和流体等多样对象。尽管已有研究独立模拟缝线或软组织，但极少涵盖完整的软组织缝合场景，尤其缺乏缝线插入过程中与可变形组织的接触建模。本文基于前期工作，提出一种新型缝合仿真环境：缝线采用位置基础动力学（PBD）建模，软体物体采用物质点法（MPM），并考虑摩擦与阻力的双向接触。我们引入PBD缝线与MPM软组织间的接触耦合方法，实现视觉上真实的缝线-组织交互。该模拟器针对GPU执行优化，支持多CUDA流并行场景，同时构建了用于自主缝合子任务（包括针头插入、推进和取出）的强化学习（RL）环境。使用ML-Agents训练的智能体表现出稳定学习能力，在最严格的距离阈值下，针头插入成功率达80%，拔针成功率为68%。
原文摘要 · Abstract (English)
Recent advances in robotics research have created a strong demand for high-performance simulators. Surgical robotics simulation faces unique challenges due to the need to model diverse objects, such as rigid instruments, soft tissue, and fluids. While many studies simulate sutures or soft tissue independently, only a few have considered the complete soft-tissue suturing scenario, including the contact between sutures and deformable tissue during suture insertion. Building on previous work, this paper presents a novel suturing simulation environment using sutures modelled by position-based dynamics (PBD) and soft bodies modelled by the material point method (MPM) while considering two-way contact with frictional and drag forces. We introduce a contact coupling method between the PBD suture and the MPM soft tissue, enabling visually plausible suture-tissue interactions. The simulator is optimized for GPU execution with parallel scenes using multiple CUDA streams, and we present a Reinforcement Learning (RL) environment for autonomous suturing sub-tasks, including needle insertion, driving, and extraction. Using ML-Agents, RL agents trained in the simulator show stable learning and achieve 80% and 68% success rates in needle insertion and extraction, respectively, under the strictest distance threshold.
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