Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation using Reinforcement Learning

TL;DR

This paper introduces a sim-to-real reinforcement learning framework for automating internal tissue point manipulation in robotic surgery, reducing surgeon workload and optimizing tissue deformation through preoperative planning.

Contribution

It presents a novel sim-to-real approach combining DRL and Bayesian optimization for surgical planning without real environment interaction.

Findings

Learned policy accurately places tissue points

Planned grasping points minimize tissue deformation

Framework generalizes to complex surgical scenarios

Abstract

Indirect simultaneous positioning (ISP), where internal tissue points are placed at desired locations indirectly through the manipulation of boundary points, is a type of subtask frequently performed in robotic surgeries. Although challenging due to complex tissue dynamics, automating the task can potentially reduce the workload of surgeons. This paper presents a sim-to-real framework for learning to automate the task without interacting with a real environment, and for planning preoperatively to find the grasping points that minimize local tissue deformation. A control policy is learned using deep reinforcement learning (DRL) in the FEM-based simulation environment and transferred to real-world situation. Grasping points are planned in the simulator by utilizing the trained policy using Bayesian optimization (BO). Inconsistent simulation performance is overcome by formulating the problem as a state augmented Markov decision process (MDP). Experimental results show that the learned policy places the internal tissue points accurately, and that the planned grasping points yield small tissue deformation among the trials. The proposed learning and planning scheme is able to automate internal tissue point manipulation in surgeries and has the potential to be generalized to complex surgical scenarios.