  Generating diverse and realistic human motion that can physically interact
with an environment remains a challenging research area in character animation.
Meanwhile, diffusion-based methods, as proposed by the robotics community, have
demonstrated the ability to capture highly diverse and multi-modal skills.
However, naively training a diffusion policy often results in unstable motions
for high-frequency, under-actuated control tasks like bipedal locomotion due to
rapidly accumulating compounding errors, pushing the agent away from optimal
training trajectories. The key idea lies in using RL policies not just for
providing optimal trajectories but for providing corrective actions in
sub-optimal states, giving the policy a chance to correct for errors caused by
environmental stimulus, model errors, or numerical errors in simulation. Our
method, Physics-Based Character Animation via Diffusion Policy (PDP), combines
reinforcement learning (RL) and behavior cloning (BC) to create a robust
diffusion policy for physics-based character animation. We demonstrate PDP on
perturbation recovery, universal motion tracking, and physics-based
text-to-motion synthesis.
