Adaptive Control of Quadruped Robot Under Varying Load Conditions

Control frameworks for legged robots often rely on accurate dynamic models. However, these models often proves to be inaccurate due to factors such as mechanical wear and tear, and unforeseen changes such as the addition of extra payloads during deployment. Significant deviations in the dynamics can severely impact the controller's performance. Our goal is to enhance the controller's model in real-time during deployment using onboard sensors and online learning. Specifically, our work focuses on quadruped locomotion under varying load conditions. This paper presents an adaptive force control framework for quadruped robots, enhanced with online system identification, to handle significant changes in both mass and center of mass (CoM). The proposed approach demonstrates superior velocity and height tracking, even under extreme load conditions, showing promise for applications in logistics, military, and rescue missions.