PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation

Authors: Mingyo Seo, Yoonyoung Cho, Yoonchang Sung, Peter Stone, Yuke Zhu, Beomjoon Kim

Published: 2024 (Conference Paper)

Source: IEEE International Conference on Robotics and Automation (ICRA)

Algorithm: PRESTO

arXiv: 2409.16012

DOI: 10.1109/ICRA55743.2025.11128590

Summary

Abstract

We introduce a learning-guided motion planning framework that generates seed trajectories using a diffusion model for trajectory optimization. Given a workspace, our method approximates the configuration space (C-space) obstacles through an environment representation consisting of a sparse set of task-related key configurations, which is then used as a conditioning input to the diffusion model. The diffusion model integrates regularization terms that encourage smooth, collision-free trajectories during training, and trajectory optimization refines the generated seed trajectories to correct any colliding segments. Our experimental results demonstrate that high-quality trajectory priors, learned through our C-space-grounded diffusion model, enable the efficient generation of collision-free trajectories in narrow-passage environments, outperforming previous learning- and planning-based baselines. Videos and additional materials can be found on the project page: https://kiwi-sherbet.github.io/PRESTO/.

Links

Primary

Paper arxiv.org

Standard

arXiv Abstract 2409.16012 arXiv PDF 2409.16012 arXiv HTML 2409.16012 DOI 10.1109/ICRA55743.2025.11128590

Alternate

kiwi-sherbet.github.io kiwi-sherbet.github.io ieeexplore.ieee.org ieeexplore.ieee.org

Tags

• PRESTO

• Motion planning

• Diffusion

• Diffusion models

• Trajectory optimization

• Collision-free