Quadruped locomotionIntermediate
PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots
In this preprint, PROMO conditions a single quadruped locomotion policy on a three-way semantic preference over tracking, stability, and energy efficiency. On a Unitree Go2, changing only the preference reduces specific energy by up to 30.4%, position error by 38.7%, and peak body-attitude deviation by 59.0% relative to a balanced setting. This gives legged robots an operator-facing trade-off dial without retraining or switching controllers.