SMPLOlympics: Sports Environments for Physically Simulated Humanoids
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911937971879936 |
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| author | Luo, Zhengyi Wang, Jiashun Liu, Kangni Zhang, Haotian Tessler, Chen Wang, Jingbo Yuan, Ye Cao, Jinkun Lin, Zihui Wang, Fengyi Hodgins, Jessica Kitani, Kris |
| author_facet | Luo, Zhengyi Wang, Jiashun Liu, Kangni Zhang, Haotian Tessler, Chen Wang, Jingbo Yuan, Ye Cao, Jinkun Lin, Zihui Wang, Fengyi Hodgins, Jessica Kitani, Kris |
| contents | We present SMPLOlympics, a collection of physically simulated environments that allow humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and standardized testing ground for evaluating and improving the capabilities of learning algorithms due to the diversity and physically demanding nature of athletic activities. As humans have been competing in these sports for many years, there is also a plethora of existing knowledge on the preferred strategy to achieve better performance. To leverage these existing human demonstrations from videos and motion capture, we design our humanoid to be compatible with the widely-used SMPL and SMPL-X human models from the vision and graphics community. We provide a suite of individual sports environments, including golf, javelin throw, high jump, long jump, and hurdling, as well as competitive sports, including both 1v1 and 2v2 games such as table tennis, tennis, fencing, boxing, soccer, and basketball. Our analysis shows that combining strong motion priors with simple rewards can result in human-like behavior in various sports. By providing a unified sports benchmark and baseline implementation of state and reward designs, we hope that SMPLOlympics can help the control and animation communities achieve human-like and performant behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SMPLOlympics: Sports Environments for Physically Simulated Humanoids Luo, Zhengyi Wang, Jiashun Liu, Kangni Zhang, Haotian Tessler, Chen Wang, Jingbo Yuan, Ye Cao, Jinkun Lin, Zihui Wang, Fengyi Hodgins, Jessica Kitani, Kris Robotics Computer Vision and Pattern Recognition Graphics We present SMPLOlympics, a collection of physically simulated environments that allow humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and standardized testing ground for evaluating and improving the capabilities of learning algorithms due to the diversity and physically demanding nature of athletic activities. As humans have been competing in these sports for many years, there is also a plethora of existing knowledge on the preferred strategy to achieve better performance. To leverage these existing human demonstrations from videos and motion capture, we design our humanoid to be compatible with the widely-used SMPL and SMPL-X human models from the vision and graphics community. We provide a suite of individual sports environments, including golf, javelin throw, high jump, long jump, and hurdling, as well as competitive sports, including both 1v1 and 2v2 games such as table tennis, tennis, fencing, boxing, soccer, and basketball. Our analysis shows that combining strong motion priors with simple rewards can result in human-like behavior in various sports. By providing a unified sports benchmark and baseline implementation of state and reward designs, we hope that SMPLOlympics can help the control and animation communities achieve human-like and performant behaviors. |
| title | SMPLOlympics: Sports Environments for Physically Simulated Humanoids |
| topic | Robotics Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2407.00187 |