CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks

• URL: http://arxiv.org/abs/2505.07261v3
• Date: Sun, 12 Oct 2025 04:52:00 GMT
• Title: CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks
• Authors: Ce Hao, Anxing Xiao, Zhiwei Xue, Harold Soh,
• Abstract summary: Diffusion-based planners have shown strong performance in short-horizon tasks but often fail in complex, long-horizon settings.<n>We propose Coupled Hierarchical Diffusion, a framework that models HL sub-goals and LL trajectories jointly within a unified diffusion process.<n> Experiments across maze navigation, tabletop manipulation, and household environments show that CHD consistently outperforms both flat and hierarchical diffusion baselines.
• Score: 10.13048343565914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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