SMPLOlympics: Sports Environments for Physically Simulated Humanoids
This provides a standardized benchmark for the control and animation communities to achieve human-like behaviors in sports simulations, though it is incremental as it builds on existing human models and methods.
The authors tackled the problem of evaluating and improving learning algorithms for humanoid control by creating SMPLOlympics, a collection of physically simulated environments for Olympic sports, and showed that combining motion priors with simple rewards can produce human-like behavior.
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.