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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.16012 |
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| _version_ | 1866915203970498560 |
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| author | Seo, Mingyo Cho, Yoonyoung Sung, Yoonchang Stone, Peter Zhu, Yuke Kim, Beomjoon |
| author_facet | Seo, Mingyo Cho, Yoonyoung Sung, Yoonchang Stone, Peter Zhu, Yuke Kim, Beomjoon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation Seo, Mingyo Cho, Yoonyoung Sung, Yoonchang Stone, Peter Zhu, Yuke Kim, Beomjoon Robotics 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. |
| title | PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.16012 |