发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于PBD与MPM的缝合模拟环境，优化GPU执行并构建RL缝合子任务环境，训练的RL智能体在进针、拔针任务中分别达到80%、68%的成功率。
AI 中文摘要
机器人学研究的最新进展对高性能模拟器产生了强烈需求。手术机器人模拟面临独特挑战，因为需要对不同物体进行建模，如刚性器械、软组织和流体。虽然许多研究分别模拟缝合线或软组织，但只有少数研究考虑了完整的软组织缝合场景，包括缝合线插入过程中缝合线与可变形组织之间的接触。在之前工作的基础上，本文提出了一种新颖的缝合模拟环境，其中缝合线采用位置动力学（PBD）建模，软组织采用物质点法（MPM）建模，同时考虑了双向接触，包含摩擦力和阻力。我们引入了PBD缝合线与MPM软组织之间的接触耦合方法，实现了视觉上合理的缝合线-组织交互。该模拟器针对GPU执行进行了优化，使用多个CUDA流实现并行场景，我们还提出了一种用于自主缝合子任务的强化学习（RL）环境，包括进针、送针和拔针。使用ML-Agents，在该模拟器中训练的RL智能体表现出稳定的学习效果，在最严格的距离阈值下，进针和拔针的成功率分别达到80%和68%。
英文摘要
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.
Comments7 pages, 9 figures, accepted for the IEEE RAS/EMBS 11th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2026)
