标题： 基于示范引导的强化学习自主软组织牵开 显示英文标题

标题： Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning

摘要： 在手术背景下，机器人可以通过执行小而重复的任务，如缝合、针头更换和组织牵开，提供重要的协助，从而使外科医生能够专注于程序的更复杂方面。 然而，现有的手术任务学习主要涉及刚体交互，而向更复杂的手术机器人的发展需要对软体进行操作。 以前的工作集中在组织模型上用于软组织任务学习，这可能成本高昂，并且是研究的进入障碍。 模拟环境为在实际组织应用之前学习手术任务提供了一种安全高效的方法。 在本研究中，我们创建了一个与机器人操作系统（ROS）兼容的物理模拟环境，支持手术任务中的刚体和软体交互。 此外，我们研究了达芬奇手术机器人患者侧机械臂促进的软组织交互。 利用pybullet物理引擎，我们模拟运动学并建立锚点，在操纵软组织时引导机械臂。 使用演示引导的强化学习（RL）算法，我们研究它们与传统强化学习算法的性能比较。 我们的计算机仿真试验展示了自主手术软组织牵开的概念验证。 结果证实了通过应用强化学习代理学习软体操作的可行性。 这项工作为未来研究开发和改进能够处理刚体和软体交互的手术机器人奠定了基础。 代码可在https://github.com/amritpal-001/tissue_retract获取。 显示英文摘要

摘要： In the context of surgery, robots can provide substantial assistance by performing small, repetitive tasks such as suturing, needle exchange, and tissue retraction, thereby enabling surgeons to concentrate on more complex aspects of the procedure. However, existing surgical task learning mainly pertains to rigid body interactions, whereas the advancement towards more sophisticated surgical robots necessitates the manipulation of soft bodies. Previous work focused on tissue phantoms for soft tissue task learning, which can be expensive and can be an entry barrier to research. Simulation environments present a safe and efficient way to learn surgical tasks before their application to actual tissue. In this study, we create a Robot Operating System (ROS)-compatible physics simulation environment with support for both rigid and soft body interactions within surgical tasks. Furthermore, we investigate the soft tissue interactions facilitated by the patient-side manipulator of the DaVinci surgical robot. Leveraging the pybullet physics engine, we simulate kinematics and establish anchor points to guide the robotic arm when manipulating soft tissue. Using demonstration-guided reinforcement learning (RL) algorithms, we investigate their performance in comparison to traditional reinforcement learning algorithms. Our in silico trials demonstrate a proof-of-concept for autonomous surgical soft tissue retraction. The results corroborate the feasibility of learning soft body manipulation through the application of reinforcement learning agents. This work lays the foundation for future research into the development and refinement of surgical robots capable of managing both rigid and soft tissue interactions. Code is available at https://github.com/amritpal-001/tissue_retract.