Whole-body Humanoid Robot Locomotion with Human Reference
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912001588985856 |
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| author | Zhang, Qiang Cui, Peter Yan, David Sun, Jingkai Duan, Yiqun Han, Gang Zhao, Wen Zhang, Weining Guo, Yijie Zhang, Arthur Xu, Renjing |
| author_facet | Zhang, Qiang Cui, Peter Yan, David Sun, Jingkai Duan, Yiqun Han, Gang Zhao, Wen Zhang, Weining Guo, Yijie Zhang, Arthur Xu, Renjing |
| contents | Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot, "Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Whole-body Humanoid Robot Locomotion with Human Reference Zhang, Qiang Cui, Peter Yan, David Sun, Jingkai Duan, Yiqun Han, Gang Zhao, Wen Zhang, Weining Guo, Yijie Zhang, Arthur Xu, Renjing Robotics Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot, "Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot. |
| title | Whole-body Humanoid Robot Locomotion with Human Reference |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.18294 |