Learning Nonprehensile Dynamic Manipulation: Sim2real Vision-Based Policy With a Surgical Robot

Abstract

Surgical tasks such as tissue retraction, tissue exposure, and needle suturing remain challenging in autonomous surgical robotics. One challenge in these tasks is nonprehensile manipulation such as pushing tissue, pressing cloth, and needle threading. In this work, we isolate the problem of nonprehensile manipulation by implementing a vision-based reinforcement learning agent for rolling a block, a task that has complex dynamics interactions, small scale objects, and a narrow field of view. We train agents in simulation with a reward formulation that encourages efficient and safe learning, domain randomization that allows for robust sim2real transfer, and a recurrent memory layer that enables reasoning about randomized dynamics parameters. We successfully transfer our agents from simulation to real and show robust execution of our vision-based policy with a 96.3% success rate. We analyze and discuss the success rate, trajectories, and recovery behaviours for various models that are either using the recurrent memory layer or are trained with a difficult physics environment.