AMBF-RL: A real-time simulation based Reinforcement Learning toolkit for Medical Robotics

Abstract

Recently, Reinforcement Learning (RL) techniques have seen significant progress in the robotics domain. This can be attributed to robust simulation frameworks that offer realistic environments to train. However, there is a lack of platforms which offer environments that are conducive to medical robotic tasks. Having earlier designed the Asynchronous Multibody Framework (AMBF) - a real-time dynamics simulator well-suited for medical robotics tasks, we propose an open source AMBF-RL (ARL) toolkit to assist in designing control algorithms for these robots, as well as a module to collect and parse expert demonstration data. We validate ARL by attempting to partially automate the task of debris removal on the da Vinci Research Kit (dVRK) Patient Side Manipulator (PSM) in simulation by calculating the optimal policy using both Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) with DDPG. The trained policies are successfully transferred onto the physical dVRK PSM and tested. Finally, we draw a conclusion from the results and discuss our observations of the experiments conducted.