CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks, Ce Hao★, Anxing Xiao, Zhiwei Xue★, Harold Soh★, Conference on Robot Learning (CoRL)
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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.
Resources
You can find our paper here.
Citation
Please consider citing our paper if you build upon our results and ideas.
Ce Hao★, Anxing Xiao, Zhiwei Xue★, Harold Soh★, “CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks”, Conference on Robot Learning (CoRL)
@hao2025chdcoupledhierarchicaldiffusion,
title={CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks}, author={Ce Hao and Anxing Xiao and Zhiwei Xue and Harold Soh}, year={2025}, eprint={2505.07261}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2505.07261}, }
Contact
If you have questions or comments, please contact Ce or Harold.
