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.
@article{arxiv.2409.16012,
title = {PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation},
author = {Mingyo Seo and Yoonyoung Cho and Yoonchang Sung and Peter Stone and Yuke Zhu and Beomjoon Kim},
journal= {arXiv preprint arXiv:2409.16012},
year = {2025}
}