SMPLOlympics：物理シミュレーションされたヒューマノイドのためのスポーツ環境
SMPLOlympics: Sports Environments for Physically Simulated Humanoids
人間の動作データと互換性のあるヒューマノイドモデルを用いて、ゴルフやテニスなど多様なオリンピック競技を物理シミュレーションで再現する環境群を構築し、強力な動作事前分布と単純な報酬の組み合わせで人間らしいスポーツ行動が学習できることを示した。
著者: Zhengyi Luo, Jiashun Wang, Kangni Liu, Haotian Zhang, Chen Tessler, Jingbo Wang, Ye Yuan, Jinkun Cao, Zihui Lin, Fengyi Wang, Jessica Hodgins, Kris Kitani
分類: cs.RO, cs.CV, cs.GR
原文アブストラクト
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.
