SLR: Learning Quadruped Locomotion without Privileged Information
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929550894563328 |
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| author | Chen, Shiyi Wan, Zeyu Yan, Shiyang Zhang, Chun Zhang, Weiyi Li, Qiang Zhang, Debing Farrukh, Fasih Ud Din |
| author_facet | Chen, Shiyi Wan, Zeyu Yan, Shiyang Zhang, Chun Zhang, Weiyi Li, Qiang Zhang, Debing Farrukh, Fasih Ud Din |
| contents | The recent mainstream reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieves high-performance control policy learning without the need for privileged information. To enhance the credibility of the proposed method's evaluation, SLR was directly compared with state-of-the-art algorithms using their open-source code repositories and original configuration parameters. Remarkably, SLR surpasses the performance of previous methods using only limited proprioceptive data, demonstrating significant potential for future applications. Ultimately, the trained policy and encoder empower the quadruped robot to traverse various challenging terrains. Videos of our results can be found on our website: https://11chens.github.io/SLR/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04835 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SLR: Learning Quadruped Locomotion without Privileged Information Chen, Shiyi Wan, Zeyu Yan, Shiyang Zhang, Chun Zhang, Weiyi Li, Qiang Zhang, Debing Farrukh, Fasih Ud Din Robotics The recent mainstream reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieves high-performance control policy learning without the need for privileged information. To enhance the credibility of the proposed method's evaluation, SLR was directly compared with state-of-the-art algorithms using their open-source code repositories and original configuration parameters. Remarkably, SLR surpasses the performance of previous methods using only limited proprioceptive data, demonstrating significant potential for future applications. Ultimately, the trained policy and encoder empower the quadruped robot to traverse various challenging terrains. Videos of our results can be found on our website: https://11chens.github.io/SLR/ |
| title | SLR: Learning Quadruped Locomotion without Privileged Information |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.04835 |