MimicAgent: Learning Quadruped Skills via Text-to-Trajectory Generation

We present MimicAgent, a text-to-trajectory generation framework for learning dynamic quadruped skills. Although reward shaping is extensively used when training quadruped policies, navigating the resulting reward landscape is notoriously difficult, requiring hours of "graduate student descent". Eureka attempts to automate reward design with LLMs, but we find that it struggles to generalize across diverse skills and morphologies. Motivated by the success of example-guided RL for humanoids, we revisit skill learning from demonstrations for quadrupeds. Unlike humanoids, which can exploit large-scale motion capture datasets for learning, quadrupeds lack such reference motion data. We make the observation that manually keyframing quadruped reference motions can be more intuitive than reward shaping; in particular, we find that rather coarse and even dynamically-infeasible motions can still be effective reference targets for example-guided RL. However, manual keyframing is still too cumbersome to create large-scale skill libraries. To address this challenge, we propose an LLM-based pipeline that generates kinematically feasible quadruped trajectories for diverse skills. Although these trajectories are not dynamically feasible, we show that they are sufficient to train successful policies. Across all evaluated skills, human raters consistently prefer policies generated by MimicAgent over those produced by Eureka.