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

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

SUBMITTER: Drago A 

PROVIDER: S-EPMC11430792 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature