Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo

Makram Chahine, T. Konstantin Rusch, Zach J Patterson, Daniela Rus
Proceedings of The 9th Conference on Robot Learning, PMLR 305:2449-2461, 2025.

Abstract

Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal exploration of the configuration space. In this paper, we propose an approach that enhances the efficiency of these methods by utilizing low-discrepancy distributions generated through Message-Passing Monte Carlo (MPMC). MPMC leverages Graph Neural Networks (GNNs) to generate point sets that uniformly cover the space, with uniformity assessed using the the $\mathcal{L}_p$-discrepancy measure, which quantifies the irregularity of sample distributions. By improving the uniformity of the point sets, our approach significantly reduces computational overhead and the number of samples required for solving motion planning problems. Experimental results demonstrate that our method outperforms traditional sampling techniques in terms of planning efficiency.

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-chahine25a, title = {Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo}, author = {Chahine, Makram and Rusch, T. Konstantin and Patterson, Zach J and Rus, Daniela}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {2449--2461}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/chahine25a/chahine25a.pdf}, url = {https://proceedings.mlr.press/v305/chahine25a.html}, abstract = {Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal exploration of the configuration space. In this paper, we propose an approach that enhances the efficiency of these methods by utilizing low-discrepancy distributions generated through Message-Passing Monte Carlo (MPMC). MPMC leverages Graph Neural Networks (GNNs) to generate point sets that uniformly cover the space, with uniformity assessed using the the $\mathcal{L}_p$-discrepancy measure, which quantifies the irregularity of sample distributions. By improving the uniformity of the point sets, our approach significantly reduces computational overhead and the number of samples required for solving motion planning problems. Experimental results demonstrate that our method outperforms traditional sampling techniques in terms of planning efficiency.} }
Endnote
%0 Conference Paper %T Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo %A Makram Chahine %A T. Konstantin Rusch %A Zach J Patterson %A Daniela Rus %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-chahine25a %I PMLR %P 2449--2461 %U https://proceedings.mlr.press/v305/chahine25a.html %V 305 %X Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal exploration of the configuration space. In this paper, we propose an approach that enhances the efficiency of these methods by utilizing low-discrepancy distributions generated through Message-Passing Monte Carlo (MPMC). MPMC leverages Graph Neural Networks (GNNs) to generate point sets that uniformly cover the space, with uniformity assessed using the the $\mathcal{L}_p$-discrepancy measure, which quantifies the irregularity of sample distributions. By improving the uniformity of the point sets, our approach significantly reduces computational overhead and the number of samples required for solving motion planning problems. Experimental results demonstrate that our method outperforms traditional sampling techniques in terms of planning efficiency.
APA
Chahine, M., Rusch, T.K., Patterson, Z.J. & Rus, D.. (2025). Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:2449-2461 Available from https://proceedings.mlr.press/v305/chahine25a.html.

Related Material