CoT Oracle: Dominant Gait Parameters Identification for Cost of Transport Prediction in Learned Quadrupedal Locomotion Policies

Abstract

Energy efficiency is a central objective in the design of reinforcement learning (RL)-based controllers for legged robots. Yet, the influence of individual gait parameters on energy consumption in learned locomotion policies remains underexplored. This paper presents a systematic sensitivity analysis of the cost of transport (CoT) across an 11-dimensional gait-parameter space for a learned quadrupedal locomotion policy deployed on the Unitree AlienGo platform. Gait commands are varied over predefined ranges, and the resulting CoT is evaluated at multiple locomotion velocities. To ensure experimental validity, we assess the policy’s ability to reliably track the commanded gait parameters. Our analysis reveals that only a subset of five parameters exerts a dominant influence on CoT, with the effect strongly modulated by locomotion speed, while the remaining six parameters contribute negligibly within the examined operating envelope. Furthermore, we show that a data-driven surrogate model–specifically, a multilayer perceptron–can accurately predict CoT directly from commanded gait parameters and velocity. These findings offer practical guidance for reducing the dimensionality of energy-aware gait optimization and enable rapid, model-based screening of efficient locomotion strategies in learned quadruped controllers.