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.

翻译：在手术场景中，机器人可通过执行缝合、换针和组织牵拉等小型重复任务提供实质性辅助，从而使外科医生能专注于手术过程中更复杂的环节。然而，现有手术任务学习主要涉及刚体交互，而更先进手术机器人的发展亟需实现软体操控能力。既往研究聚焦于用于软组织任务学习的组织模型，这类方法成本高昂且成为科研入门障碍。仿真环境为手术任务学习提供了安全高效的途径，使其在实际组织应用前即可进行训练。本研究构建了一个兼容机器人操作系统（ROS）的物理仿真环境，支持手术任务中刚体与软体的交互。我们进一步研究了达芬奇手术机器人患者侧机械臂所实现的软组织交互机制。基于PyBullet物理引擎，我们仿真了运动学过程并建立锚点以引导机械臂操控软组织。通过采用示教引导的强化学习算法，我们将其与传统强化学习算法进行性能对比。硅胶仿真实验验证了自主手术软组织牵拉的概念可行性，结果证实了利用强化学习智能体学习软体操控的可行性。本研究为开发能处理刚体与软组织交互的手术机器人奠定了基础，相关代码见https://github.com/amritpal-001/tissue_retract。