  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.
