  We propose MIMOC: Motion Imitation from Model-Based Optimal Control. MIMOC is
a Reinforcement Learning (RL) controller that learns agile locomotion by
imitating reference trajectories from model-based optimal control. MIMOC
mitigates challenges faced by other motion imitation RL approaches because the
references are dynamically consistent, require no motion retargeting, and
include torque references. Hence, MIMOC does not require fine-tuning. MIMOC is
also less sensitive to modeling and state estimation inaccuracies than
model-based controllers. We validate MIMOC on the Mini-Cheetah in outdoor
environments over a wide variety of challenging terrain, and on the MIT
Humanoid in simulation. We show cases where MIMOC outperforms model-based
optimal controllers, and show that imitating torque references improves the
policy's performance.
