Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery

TL;DR

This paper develops a monocular image-based imitation learning approach for autonomous robotic peg transfer tasks in laparoscopic surgery, addressing depth perception challenges without requiring depth sensors.

Contribution

It introduces a method to extract motion constraints from expert demonstrations to improve imitation learning with monocular images in surgical robotics.

Findings

Successful implementation with Franka Emika Panda Robots

Enhanced accuracy in peg transfer tasks

Effective depth perception without additional sensors

Abstract

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.