Active Learning of Dynamics for Data-Driven Control Using Koopman Operators
This work addresses the challenge of data-efficient control for robotic systems, offering a method that combines active learning with Koopman operators, though it appears incremental as it builds on existing Koopman operator frameworks.
The paper tackles the problem of learning dynamics for robotic control by introducing an active learning strategy that uses Koopman operators to represent nonlinear systems as linear ones, resulting in faster learning and improved control performance, as demonstrated with a quadcopter achieving single-execution active learning and stabilization during free-fall.
This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by taking advantage of the Koopman operator representation. We first motivate the use of representing nonlinear systems as linear Koopman operator systems by illustrating the improved model-based control performance with an actuated Van der Pol system. Information-theoretic methods are then applied to the Koopman operator formulation of dynamical systems where we derive a controller for active learning of robot dynamics. The active learning controller is shown to increase the rate of information about the Koopman operator. In addition, our active learning controller can readily incorporate policies built on the Koopman dynamics, enabling the benefits of fast active learning and improved control. Results using a quadcopter illustrate single-execution active learning and stabilization capabilities during free-fall. The results for active learning are extended for automating Koopman observables and we implement our method on real robotic systems.