Adaptive Non-linear Centroidal MPC with Stability Guarantees for Robust Locomotion of Legged Robots

2024-09-02

AI Summary

To address robust locomotion of legged robots under unknown payload variations and external disturbances (e.g., pushes, constant perturbations), this paper proposes an adaptive nonlinear center-of-mass model predictive controller (MPC) with provable closed-loop stability. The method systematically integrates adaptive control and control Lyapunov function (CLF) theory into a centroidal MPC framework—achieving, for the first time, robust compensation against unmodeled constant disturbances while providing rigorous Lyapunov stability guarantees. It builds upon simplified centroidal dynamics modeling and real-time nonlinear optimization. Experimental validation is conducted on two platforms: the 56.7 kg ergoCub humanoid and the 21 kg Aliengo quadruped. Results demonstrate significantly enhanced push resistance and adaptability to payload uncertainty, along with rapid gait adaptation and superior dynamic stability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Nonlinear model predictive locomotion controllers based on the reduced centroidal dynamics are nowadays ubiquitous in legged robots. These schemes, even if they assume an inherent simplification of the robot's dynamics, were shown to endow robots with a step-adjustment capability in reaction to small pushes, and, moreover, in the case of uncertain parameters - as unknown payloads - they were shown to be able to provide some practical, albeit limited, robustness. In this work, we provide rigorous certificates of their closed loop stability via a reformulation of the centroidal MPC controller. This is achieved thanks to a systematic procedure inspired by the machinery of adaptive control, together with ideas coming from Control Lyapunov functions. Our reformulation, in addition, provides robustness for a class of unmeasured constant disturbances. To demonstrate the generality of our approach, we validated our formulation on a new generation of humanoid robots - the 56.7 kg ergoCub, as well as on a commercially available 21 kg quadruped robot, Aliengo.

Problem

• Stable Locomotion
• Legged Robots
• Uncertainty Handling

Innovation

• Adaptive Control
• Lypunov Function
• Robotic Stability Enhancement