2024-04-18-cosier24a.md

title	software	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
A Unifying Variational Framework for Gaussian Process Motion Planning	https://github.com/luke-ck/vgpmp	To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must therefore balance competing demands, and should ideally incorporate uncertainty to handle noise, model errors, and facilitate deployment in complex environments. To address these issues, we introduce a framework for robot motion planning based on variational Gaussian processes, which unifies and generalizes various probabilistic-inference-based motion planning algorithms, and connects them with optimization-based planners. Our framework provides a principled and flexible way to incorporate equality-based, inequality-based, and soft motion-planning constraints during end-to-end training, is straightforward to implement, and provides both interval-based and Monte-Carlo-based uncertainty estimates. We conduct experiments using different environments and robots, comparing against baseline approaches based on the feasibility of the planned paths, and obstacle avoidance quality. Results show that our proposed approach yields a good balance between success rates and path quality.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	cosier24a	0	A Unifying Variational Framework for {G}aussian Process Motion Planning	1315	1323	1315-1323	1315	false	Cosier, Lucas C. and Iordan, Rares and Zwane, Sicelukwanda N. T. and Franzese, Giovanni and Wilson, James T. and Deisenroth, Marc and Terenin, Alexander and Bekiroglu, Yasemin	given family Lucas C. Cosier given family Rares Iordan given family Sicelukwanda N. T. Zwane given family Giovanni Franzese given family James T. Wilson given family Marc Deisenroth given family Alexander Terenin given family Yasemin Bekiroglu	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/cosier24a/cosier24a.pdf