Robust Swimming Controller for Soft Robots via Drop-Out Learning

Abstract

A novel framework for training a robotic fish to learn how to swim, even in the presence of degradations or failures in actuators is developed. Robotic underwater robots, particularly soft fish-inspired designs have gained significant attention due to their distinct benefits, including superior maneuverability, energy efficiency, versatile applications, and seamless integration with marine environments. However, their material properties and actuators can degrade, leading to pre-mature system failures. In this paper, we introduce the concept of actuator drop-out during training, to enable the robot to learn how to swim even when one or more actuators are degraded or non-functional. A Soft Actor-Critic Deep Reinforcement Learning architecture is used to learn a policy, with actuator degradations/failures introduced during training. A four actuator koi fish is modeled and simulated using the FishGym environment. Navigation-based validation tests show little degradation with one actuator failure, and much more robust swimming behaviors and performance compared to training with no failures, even when two or three actuators fail. These results will improve long-term operational reliability, ensuring robot fish functionality even in challenging underwater conditions.