Journal article
Using Reinforcement Learning to Develop a Novel Gait for a Bio-Robotic California Sea Lion
Biomimetics (Basel, Switzerland), v 9(9), p522
30 Aug 2024
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.
Metrics
Details
- Title
- Using Reinforcement Learning to Develop a Novel Gait for a Bio-Robotic California Sea Lion
- Creators
- Anthony Drago - Drexel UniversityShraman Kadapa - Drexel UniversityNicholas Marcouiller - Drexel UniversityHarry G. Kwatny - Drexel UniversityJames L. Tangorra - Drexel University
- Publication Details
- Biomimetics (Basel, Switzerland), v 9(9), p522
- Publisher
- MDPI
- Number of pages
- 24
- Grant note
- ONR: N00014-21-1-2133
This research was funded by the Office of Naval Research (Dr Thomas McKenna, Program Officer, ONR Code 341) grant number N00014-21-1-2133.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:001323219200001
- Scopus ID
- 2-s2.0-85205073633
- Other Identifier
- 991021902515104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Multidisciplinary
- Materials Science, Biomaterials