2023-12-02-lee23a.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
Equivariant Motion Manifold Primitives	Poster	psyvs5wdAV	Existing movement primitive models for the most part focus on representing and generating a single trajectory for a given task, limiting their adaptability to situations in which unforeseen obstacles or new constraints may arise. In this work we propose Motion Manifold Primitives (MMP), a movement primitive paradigm that encodes and generates, for a given task, a continuous manifold of trajectories each of which can achieve the given task. To address the challenge of learning each motion manifold from a limited amount of data, we exploit inherent symmetries in the robot task by constructing motion manifold primitives that are equivariant with respect to given symmetry groups. Under the assumption that each of the MMPs can be smoothly deformed into each other, an autoencoder framework is developed to encode the MMPs and also generate solution trajectories. Experiments involving synthetic and real-robot examples demonstrate that our method outperforms existing manifold primitive methods by significant margins. Code is available at https://github.com/dlsfldl/EMMP-public.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lee23a	0	Equivariant Motion Manifold Primitives	1199	1221	1199-1221	1199	false	Lee, Byeongho and Lee, Yonghyeon and Kim, Seungyeon and Son, MinJun and Park, Frank C.	given family Byeongho Lee given family Yonghyeon Lee given family Seungyeon Kim given family MinJun Son given family Frank C. Park	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/lee23a/lee23a.pdf