A Novel Continuum Manipulator

Abstract

We address the problem of controlling continuum manipulators and evaluate Reinforcement Learning to produce a control policy for a robotic platform. Our approach discretizes the state and action spaces to reduce the training needed to converge to an optimal policy. We integrate Q-Learning, computer vision, and a pneumatic system into a single robotic platform. The agent is tasked with tracking and striking a target with a continuum manipulator modeled by a party-blower. We describe Reinforcement Learning, the methods used to train the agent, and describe the performance of the optimal policy successfully striking the target.