2023-12-02-liu23e.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
Composable Part-Based Manipulation	Poster	o-K3HVUeEw	In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills. By considering the functional correspondences between object parts, we conceptualize functional actions, such as pouring and constrained placing, as combinations of different correspondence constraints. CPM comprises a collection of composable diffusion models, where each model captures a different inter-object correspondence. These diffusion models can generate parameters for manipulation skills based on the specific object parts. Leveraging part-based correspondences coupled with the task decomposition into distinct constraints enables strong generalization to novel objects and object categories. We validate our approach in both simulated and real-world scenarios, demonstrating its effectiveness in achieving robust and generalized manipulation capabilities.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	liu23e	0	Composable Part-Based Manipulation	1300	1315	1300-1315	1300	false	Liu, Weiyu and Mao, Jiayuan and Hsu, Joy and Hermans, Tucker and Garg, Animesh and Wu, Jiajun	given family Weiyu Liu given family Jiayuan Mao given family Joy Hsu given family Tucker Hermans given family Animesh Garg given family Jiajun Wu	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/liu23e/liu23e.pdf