Hardware-in-the-Loop Soft Robotic Testing Framework Using an Actor-Critic Deep Reinforcement Learning Algorithm

摘要

Polymer-based soft robots are difficult to characterize due to their non-linear nature. This difficulty is compounded by multiple additional degrees of movement freedom which adds complexity to any control strategy proposed. The following work proposes and demonstrates a modular framework to test, debug and characterize soft robots using the robot operating system (ROS), to enable modeless deep reinforcement learning control strategies through hardware-in-the-loop system training. The framework is demonstrated using an actor-critic algorithm to learn a locomotion policy for a two-actuator pneu-net soft robot with integrated resistive flex sensors. The result of convergent locomotion studies was an 89.5% increase in the likelihood of reaching the end of frame design goal versus random oracle actuation vectors.