2023-12-02-kulshrestha23a.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
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning	Poster	D0X97ODIYK	Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments. Existing work focuses primarily on single-level rearrangement planning and, even if multiple levels exist, dependency relations among substructures are geometrically simpler, like tower stacking. We propose Structural Concept Learning (SCL), a deep learning approach that leverages graph attention networks to perform multi-level object rearrangement planning for scenes with structural dependency hierarchies. It is trained on a self-generated simulation data set with intuitive structures, works for unseen scenes with an arbitrary number of objects and higher complexity of structures, infers independent substructures to allow for task parallelization over multiple manipulators, and generalizes to the real world. We compare our method with a range of classical and model-based baselines to show that our method leverages its scene understanding to achieve better performance, flexibility, and efficiency. The dataset, demonstration videos, supplementary details, and code implementation are available at: https://manavkulshrestha.github.io/scl	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kulshrestha23a	0	Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning	3180	3193	3180-3193	3180	false	Kulshrestha, Manav and Qureshi, Ahmed H.	given family Manav Kulshrestha given family Ahmed H. Qureshi	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/kulshrestha23a/kulshrestha23a.pdf