Task-Oriented Koopman-Based Control with Contrastive Encoder

Autor:	Lyu, Xubo, Hu, Hanyang, Siriya, Seth, Pu, Ye, Chen, Mo
Rok vydání:	2023
Předmět:	Computer Science - Robotics Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop. By prioritizing the task cost as the main objective for controller learning, we reduce the reliance of controller design on a well-identified model, which, for the first time to the best of our knowledge, extends Koopman control from low to high-dimensional, complex nonlinear systems, including pixel-based tasks and a real robot with lidar observations. Code and videos are available \href{https://sites.google.com/view/kpmlilatsupp/}{here}. Comment: Accepted by the 7th Annual Conference on Robot Learning (CoRL), 2023 (oral spotlight)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.16077 Zobrazit plný text záznamu View this record from Arxiv