Version 1
: Received: 25 May 2024 / Approved: 27 May 2024 / Online: 27 May 2024 (10:12:00 CEST)
How to cite:
Drago, A.; Kadapa, S.; Marcouiller, N.; Kwatny, H. G.; Tangorra, J. L. Using Reinforcement Learning to Develop a Novel Gait for a Bio-robotic California Sea Lion. Preprints2024, 2024051672. https://doi.org/10.20944/preprints202405.1672.v1
Drago, A.; Kadapa, S.; Marcouiller, N.; Kwatny, H. G.; Tangorra, J. L. Using Reinforcement Learning to Develop a Novel Gait for a Bio-robotic California Sea Lion. Preprints 2024, 2024051672. https://doi.org/10.20944/preprints202405.1672.v1
Drago, A.; Kadapa, S.; Marcouiller, N.; Kwatny, H. G.; Tangorra, J. L. Using Reinforcement Learning to Develop a Novel Gait for a Bio-robotic California Sea Lion. Preprints2024, 2024051672. https://doi.org/10.20944/preprints202405.1672.v1
APA Style
Drago, A., Kadapa, S., Marcouiller, N., Kwatny, H. G., & Tangorra, J. L. (2024). Using Reinforcement Learning to Develop a Novel Gait for a Bio-robotic California Sea Lion. Preprints. https://doi.org/10.20944/preprints202405.1672.v1
Chicago/Turabian Style
Drago, A., Harry G Kwatny and James L. Tangorra. 2024 "Using Reinforcement Learning to Develop a Novel Gait for a Bio-robotic California Sea Lion" Preprints. https://doi.org/10.20944/preprints202405.1672.v1
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 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
