2023-12-02-abyaneh23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation	Poster	Pwsm7d0iWJD	Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert’s behavior. However, relying solely on the expert’s data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided utilizing nonlinear dynamical systems, acting as high-level motion planners, in conjunction with the Lyapunov stability theorem. Yet, these methods are prone to inaccurate policies, high computational cost, sample inefficiency, or quasi stability when replicating complex and highly nonlinear trajectories. To mitigate this problem, we present an approach for learning a globally stable nonlinear dynamical system as a motion planning policy. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial’s coefficients jointly with a Lyapunov candidate. To showcase its success, we compare our method against the state of the art in simulation and conduct real-world experiments with the Kinova Gen3 Lite manipulator arm. Our experiments demonstrate the sample efficiency and reproduction accuracy of our method for various expert trajectories, while remaining stable in the face of perturbations.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	abyaneh23a	0	Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation	2642	2662	2642-2662	2642	false	Abyaneh, Amin and Lin, Hsiu-Chin	given family Amin Abyaneh given family Hsiu-Chin Lin	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/abyaneh23a/abyaneh23a.pdf