2023-12-02-mao23a.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 Reusable Manipulation Strategies	Poster	ihqTtzS83VS	Humans demonstrate an impressive ability to acquire and generalize manipulation “tricks.” Even from a single demonstration, such as using soup ladles to reach for distant objects, we can apply this skill to new scenarios involving different object positions, sizes, and categories (e.g., forks and hammers). Additionally, we can flexibly combine various skills to devise long-term plans. In this paper, we present a framework that enables machines to acquire such manipulation skills, referred to as “mechanisms,” through a single demonstration and self-play. Our key insight lies in interpreting each demonstration as a sequence of changes in robot-object and object-object contact modes, which provides a scaffold for learning detailed samplers for continuous parameters. These learned mechanisms and samplers can be seamlessly integrated into standard task and motion planners, enabling their compositional use.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	mao23a	0	Learning Reusable Manipulation Strategies	1467	1483	1467-1483	1467	false	Mao, Jiayuan and Lozano-P\'{e}rez, Tom\'{a}s and Tenenbaum, Joshua B. and Kaelbling, Leslie Pack	given family Jiayuan Mao given family Tomás Lozano-Pérez given family Joshua B. Tenenbaum given family Leslie Pack Kaelbling	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/mao23a/mao23a.pdf