Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning

Understand

In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real reinforcement learning.

• Our approach is to utilize a single rigid-body model (SRBM) to optimize libraries of trajectories offline to be used as expert references in the reward function of a learned policy.
• This method translates the model's dynamically rich rotational and translational behaviour to a full-order robot model and successfully transfers to real hardware.
• The SRBM's simplicity allows for fast iteration and refinement of behaviors, while the robustness of learning-based controllers allows for highly dynamic motions to be transferred to hardware.