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.

翻译：本文提出SMPLOlympics——一个允许人形体在多种奥林匹克运动项目中竞技的物理仿真环境集合。由于体育活动的多样性与高物理要求特性，运动仿真为评估和改进学习算法能力提供了丰富且标准化的测试平台。鉴于人类在这些运动项目中已竞争多年，关于实现更优表现的策略选择亦存在大量现有知识。为利用视频与动作捕捉中现有的人类示范数据，我们将人形体设计为与视觉和图形学界广泛采用的SMPL及SMPL-X人体模型兼容。我们提供包括高尔夫、标枪、跳高、跳远、跨栏等单项运动环境，以及包含乒乓球、网球、击剑、拳击、足球、篮球等1v1与2v2对抗性项目的竞技运动环境。分析表明，将强运动先验与简单奖励相结合能在各类运动中产生类人行为。通过提供统一的运动基准测试平台以及状态与奖励设计的基线实现，我们希望SMPLOlympics能助力控制与动画领域实现类人且高效的行为表现。