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Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots by Li, Feihan; Abuduweili, Abulikemu; Sun, Yifan; Chen, Rui; Zhao, Weiye; Liu, Changliu is a scholarly article available to read on EtoBox.

What is Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots about?

The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems can be effectively controlled using methods like Model Predictive Control (MPC), the control of nonlinear systems remains complex. One promising solution is the Koopman Operator, which approximates nonlinear dynamics with a linear model, enabling the use of proven linear control techniques. However, achieving accurate linearization through data-driven methods is difficult due to issues like approximation error, domain shifts, and the limitations of fixed linear state-space representations. These challenges restrict the scalability of Koopman-based approaches. This paper addresses these challenges by proposing a continual learning algorithm designed to iteratively refine Koopman dynamics for high-dimensional legged robots. The key idea is to progressively expand the dataset and latent space dimension, enabling the learned Koopman dynamics to converge towards accurate approximations of the true system dynamics. Theoretical analysis shows that the linear approximation error of our method converges monotonic

Author

Li, Feihan; Abuduweili, Abulikemu; Sun, Yifan; Chen, Rui; Zhao, Weiye; Liu, Changliu

Published

2024

Language

EN