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

Jesus Marquez; Charles Sullivan; Ryan M. Price; Robert C. Roberts

RA-L 20236 citations

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

Jesus Marquez, Charles Sullivan, Ryan M. Price, Robert C. Roberts

Abstract

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.

BibTeX

@inproceedings{ral2023_hardwareintheloo,
  title = {Hardware-in-the-Loop Soft Robotic Testing Framework Using an Actor-Critic Deep Reinforcement Learning Algorithm},
  author = {Jesus Marquez and Charles Sullivan and Ryan M. Price and Robert C. Roberts},
  booktitle = {RA-L 2023},
  year = {2023}
}