Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909244603760640 |
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| author | Thibault, William Melek, William Mombaur, Katja |
| author_facet | Thibault, William Melek, William Mombaur, Katja |
| contents | Humanoid locomotion is a key skill to bring humanoids out of the lab and into the real-world. Many motion generation methods for locomotion have been proposed including reinforcement learning (RL). RL locomotion policies offer great versatility and generalizability along with the ability to experience new knowledge to improve over time. This work presents a velocity-based RL locomotion policy for the REEM-C robot. The policy uses a periodic reward formulation and is implemented in Brax/MJX for fast training. Simulation results for the policy are demonstrated with future experimental results in progress. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_05148 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX Thibault, William Melek, William Mombaur, Katja Robotics Humanoid locomotion is a key skill to bring humanoids out of the lab and into the real-world. Many motion generation methods for locomotion have been proposed including reinforcement learning (RL). RL locomotion policies offer great versatility and generalizability along with the ability to experience new knowledge to improve over time. This work presents a velocity-based RL locomotion policy for the REEM-C robot. The policy uses a periodic reward formulation and is implemented in Brax/MJX for fast training. Simulation results for the policy are demonstrated with future experimental results in progress. |
| title | Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.05148 |