Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913906044174336 |
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| author | Humphreys, Joseph Zhou, Chengxu |
| author_facet | Humphreys, Joseph Zhou, Chengxu |
| contents | Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-gait transition strategies, gait memory and real-time motion adjustments enables a DRL control framework to fluidly switch among multiple gaits and recover from instability, all without external sensing. Our framework is guided by biomechanics-inspired metrics that capture efficiency, stability and system limits, which are unified to inform optimal gait selection. The resulting framework achieves blind zero-shot deployment across diverse, real-world terrains and substantially significantly outperforms baseline controllers. By embedding biological principles into data-driven control, this work marks a step towards robust, efficient and versatile robotic locomotion, highlighting how animal motor intelligence can shape the next generation of adaptive machines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion Humphreys, Joseph Zhou, Chengxu Robotics Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-gait transition strategies, gait memory and real-time motion adjustments enables a DRL control framework to fluidly switch among multiple gaits and recover from instability, all without external sensing. Our framework is guided by biomechanics-inspired metrics that capture efficiency, stability and system limits, which are unified to inform optimal gait selection. The resulting framework achieves blind zero-shot deployment across diverse, real-world terrains and substantially significantly outperforms baseline controllers. By embedding biological principles into data-driven control, this work marks a step towards robust, efficient and versatile robotic locomotion, highlighting how animal motor intelligence can shape the next generation of adaptive machines. |
| title | Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.09440 |