CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911205145182208 |
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| author | Hao, Ce Xiao, Anxing Xue, Zhiwei Soh, Harold |
| author_facet | Hao, Ce Xiao, Anxing Xue, Zhiwei Soh, Harold |
| contents | Diffusion-based planners have shown strong performance in short-horizon tasks but often fail in complex, long-horizon settings. We trace the failure to loose coupling between high-level (HL) sub-goal selection and low-level (LL) trajectory generation, which leads to incoherent plans and degraded performance. We propose Coupled Hierarchical Diffusion (CHD), a framework that models HL sub-goals and LL trajectories jointly within a unified diffusion process. A shared classifier passes LL feedback upstream so that sub-goals self-correct while sampling proceeds. This tight HL-LL coupling improves trajectory coherence and enables scalable long-horizon diffusion planning. Experiments across maze navigation, tabletop manipulation, and household environments show that CHD consistently outperforms both flat and hierarchical diffusion baselines. Our website is: https://sites.google.com/view/chd2025/home |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks Hao, Ce Xiao, Anxing Xue, Zhiwei Soh, Harold Robotics Artificial Intelligence Diffusion-based planners have shown strong performance in short-horizon tasks but often fail in complex, long-horizon settings. We trace the failure to loose coupling between high-level (HL) sub-goal selection and low-level (LL) trajectory generation, which leads to incoherent plans and degraded performance. We propose Coupled Hierarchical Diffusion (CHD), a framework that models HL sub-goals and LL trajectories jointly within a unified diffusion process. A shared classifier passes LL feedback upstream so that sub-goals self-correct while sampling proceeds. This tight HL-LL coupling improves trajectory coherence and enables scalable long-horizon diffusion planning. Experiments across maze navigation, tabletop manipulation, and household environments show that CHD consistently outperforms both flat and hierarchical diffusion baselines. Our website is: https://sites.google.com/view/chd2025/home |
| title | CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.07261 |