Behavior evolution-inspired approach to walking gait reinforcement training for quadruped robots Article Swipe
Yu Wang
,
Wenchuan Jia
,
Yi Sun
,
Dong He
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.16862
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.16862
Reinforcement learning method is extremely competitive in gait generation techniques for quadrupedal robot, which is mainly due to the fact that stochastic exploration in reinforcement training is beneficial to achieve an autonomous gait. Nevertheless, although incremental reinforcement learning is employed to improve training success and movement smoothness by relying on the continuity inherent during limb movements, challenges remain in adapting gait policy to diverse terrain and external disturbance. Inspired by the association between reinforcement learning and the evolution of animal motion behavior, a self-improvement mechanism for reference gait is introduced in this paper to enable incremental learning of action and self-improvement of reference action together to imitate the evolution of animal motion behavior. Further, a new framework for reinforcement training of quadruped gait is proposed. In this framework, genetic algorithm is specifically adopted to perform global probabilistic search for the initial value of the arbitrary foot trajectory to update the reference trajectory with better fitness. Subsequently, the improved reference gait is used for incremental reinforcement learning of gait. The above process is repeatedly and alternatively executed to finally train the gait policy. The analysis considering terrain, model dimensions, and locomotion condition is presented in detail based on simulation, and the results show that the framework is significantly more adaptive to terrain compared to regular incremental reinforcement learning.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.16862
- https://arxiv.org/pdf/2409.16862
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403784597
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403784597Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2409.16862Digital Object Identifier
- Title
-
Behavior evolution-inspired approach to walking gait reinforcement training for quadruped robotsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-25Full publication date if available
- Authors
-
Yu Wang, Wenchuan Jia, Yi Sun, Dong HeList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.16862Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2409.16862Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2409.16862Direct OA link when available
- Concepts
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Gait, Reinforcement learning, Robot, Reinforcement, Training (meteorology), Computer science, Gait training, Physical medicine and rehabilitation, Artificial intelligence, Psychology, Physics, Neuroscience, Social psychology, Rehabilitation, Medicine, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Reinforcement | 0 |
| abstract_inverted_index.Subsequently, | 155 |
| abstract_inverted_index.alternatively | 174 |
| abstract_inverted_index.probabilistic | 136 |
| abstract_inverted_index.reinforcement | 24, 36, 73, 118, 164, 215 |
| abstract_inverted_index.significantly | 206 |
| abstract_inverted_index.self-improvement | 83, 100 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |