ExBody2: Advanced Expressive Humanoid Whole-Body Control
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913730835513344 |
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| author | Ji, Mazeyu Peng, Xuanbin Liu, Fangchen Li, Jialong Yang, Ge Cheng, Xuxin Wang, Xiaolong |
| author_facet | Ji, Mazeyu Peng, Xuanbin Liu, Fangchen Li, Jialong Yang, Ge Cheng, Xuxin Wang, Xiaolong |
| contents | This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We propose Advanced Expressive Whole-Body Control (Exbody2), a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data and then transferred to the real world. We introduce a technique for decoupling the velocity tracking of the entire body from tracking body landmarks. We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics and to automatically filter away infeasible whole-body motions. This two-step approach enabled us to produce a student policy that can be deployed on the robot that can walk, crouch, and dance. We also provide insight into the trade-off between versatility and the tracking performance on specific motions. We observed significant improvement of tracking performance after fine-tuning on a small amount of data, at the expense of the others. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ExBody2: Advanced Expressive Humanoid Whole-Body Control Ji, Mazeyu Peng, Xuanbin Liu, Fangchen Li, Jialong Yang, Ge Cheng, Xuxin Wang, Xiaolong Robotics Artificial Intelligence Machine Learning This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We propose Advanced Expressive Whole-Body Control (Exbody2), a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data and then transferred to the real world. We introduce a technique for decoupling the velocity tracking of the entire body from tracking body landmarks. We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics and to automatically filter away infeasible whole-body motions. This two-step approach enabled us to produce a student policy that can be deployed on the robot that can walk, crouch, and dance. We also provide insight into the trade-off between versatility and the tracking performance on specific motions. We observed significant improvement of tracking performance after fine-tuning on a small amount of data, at the expense of the others. |
| title | ExBody2: Advanced Expressive Humanoid Whole-Body Control |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.13196 |