Learning to Hop for a Single-Legged Robot with Parallel Mechanism
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912197137924096 |
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| author | Zhang, Hongbo Chu, Xiangyu Chen, Yanlin Tang, Yunxi Yue, Linzhu Liu, Yun-Hui Au, Kwok Wai Samuel |
| author_facet | Zhang, Hongbo Chu, Xiangyu Chen, Yanlin Tang, Yunxi Yue, Linzhu Liu, Yun-Hui Au, Kwok Wai Samuel |
| contents | This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism. Unlike serial mechanisms, parallel mechanisms can not be accurately simulated due to the complexity of their kinematic constraints and closed-loop structures. Besides, learning to hop suffers from prolonged aerial phase and the sparse nature of the rewards. To address them, we propose a learning framework to encode long-history feedback to account for the under-actuation brought by the prolonged aerial phase. In the proposed framework, we also introduce a simplified serial configuration for the parallel design to avoid directly simulating parallel structure during the training. A torque-level conversion is designed to deal with the parallel-serial conversion to handle the sim-to-real issue. Simulation and hardware experiments have been conducted to validate this framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Hop for a Single-Legged Robot with Parallel Mechanism Zhang, Hongbo Chu, Xiangyu Chen, Yanlin Tang, Yunxi Yue, Linzhu Liu, Yun-Hui Au, Kwok Wai Samuel Robotics This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism. Unlike serial mechanisms, parallel mechanisms can not be accurately simulated due to the complexity of their kinematic constraints and closed-loop structures. Besides, learning to hop suffers from prolonged aerial phase and the sparse nature of the rewards. To address them, we propose a learning framework to encode long-history feedback to account for the under-actuation brought by the prolonged aerial phase. In the proposed framework, we also introduce a simplified serial configuration for the parallel design to avoid directly simulating parallel structure during the training. A torque-level conversion is designed to deal with the parallel-serial conversion to handle the sim-to-real issue. Simulation and hardware experiments have been conducted to validate this framework. |
| title | Learning to Hop for a Single-Legged Robot with Parallel Mechanism |
| topic | Robotics |
| url | https://arxiv.org/abs/2501.11945 |