Work

Using Reinforcement Learning to Develop a Novel Gait for a Bio-Robotic California Sea Lion

Year: 2024

Type: article

Abstract: While researchers have made notable progress in bio-inspired swimming robot development, a persistent challenge lies in creating propulsive gaits tailored to these robotic systems. The California sea lion achieves its robust swimming abilities through a careful coordination of foreflippers and body segments. In this paper, reinforcement learning (RL) was used to develop a novel sea lion foreflipper gait for a bio-robotic swimmer using a numerically modelled computational representation of the robot. This model integration enabled reinforcement learning to develop desired swimming gaits in the challenging underwater domain. The novel RL gait outperformed the characteristic sea lion foreflipper gait in the simulated underwater domain. When applied to the real-world robot, the RL constructed novel gait performed as well as or better than the characteristic sea lion gait in many factors. This work shows the potential for using complimentary bio-robotic and numerical models with reinforcement learning to enable the development of effective gaits and maneuvers for underwater swimming vehicles.

Source: Biomimetics

DOI: 10.3390/biomimetics9090522

Authors: Anthony Drago, Shraman Kadapa, Nicholas Marcouiller, Harry G Kwatny, James L. Tangorra

Institution: Drexel University

Language: English

Topic: Biomimetic flight and propulsion mechanisms

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,501

Funders: Smithsonian Institution, George Washington University, West Chester University, Office of Naval Research