In this study, we present an implementation strategy for a robot that performs peg transfer tasks in Fundamentals of Laparoscopic Surgery (FLS) via imitation learning, aimed at the development of an autonomous robot for laparoscopic surgery. Robotic laparoscopic surgery presents two main challenges: (1) the need to manipulate forceps using ports established on the body surface as fulcrums, and (2) difficulty in perceiving depth information when working with a monocular camera that displays its images on a monitor. Especially, regarding issue (2), most prior research has assumed the availability of depth images or models of a target to be operated on. Therefore, in this study, we achieve more accurate imitation learning with only monocular images by extracting motion constraints from one exemplary motion of skilled operators, collecting data based on these constraints, and conducting imitation learning based on the collected data. We implemented an overall system using two Franka Emika Panda Robot Arms and validated its effectiveness.

翻译：本研究提出了一种通过模仿学习执行腹腔镜手术基础（FLS）中Peg转移任务的机器人实现策略，旨在为腹腔镜手术开发自主机器人。机器人腹腔镜手术面临两大挑战：（1）需使用以体表开口为支点的器械进行操作；（2）在通过单目摄像头将图像显示于监视器时，难以感知深度信息。特别针对问题（2），以往多数研究假设可获得深度图像或目标操作模型。因此，本研究通过从熟练操作者的示范动作中提取运动约束，基于这些约束收集数据，并基于所采集数据进行模仿学习，仅利用单目图像实现了更精确的模仿学习。我们使用两台Franka Emika Panda机器人手臂构建了完整系统，并验证了其有效性。