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arXiv · 2409.16012

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

Mingyo Seo·Yoonyoung Cho·Yoonchang Sung·Peter Stone·Yuke Zhu·Beomjoon Kim
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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.

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Mingyo Seo, Yoonyoung Cho, Yoonchang Sung, Peter Stone, Yuke Zhu, Beomjoon Kim. 2024-09-24. PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation. https://arxiv.org/abs/2409.16012

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