[2605.04525] HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
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Computer Science > Robotics
arXiv:2605.04525 (cs)
[Submitted on 6 May 2026]
Title:HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
Authors:Nandiraju Gireesh, Yuanliang Ju, Chaoyi Xu, Weiheng Liu, Yuxuan Wan, He Wang
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Abstract:Recent advances in generative models have shown promise in generating behavior plans for long-horizon, sparse reward tasks. While these approaches have achieved promising results, they often lack a principled framework for hierarchical decomposition and struggle with the computational demands of real-time execution, due to their iterative denoising process. In this work, we introduce Hierarchical Diffusion-Flow (HDFlow), a novel hierarchical planning framework that optimally leverages the strengths of diffusion and rectified flow models to overcome the limitations of single-paradigm generative planners. HDFlow employs a high-level diffusion planner to generate sequences of strategic subgoals in a learned latent space, capitalizing on diffusion's powerful exploratory capabilities. These subgoals then guide a low-level rectified flow planner that generates smooth and dense trajectories, exploiting the speed and efficiency of ordinary differential equation (ODE)-based trajectory generation. We evaluate HDFlow on four challenging furniture assembly tasks in both simulation and real-world, where it significantly outperforms state-of-the-art methods. Furthermore, we also showcase our method's generalizability on two long-horizon benchmarks comprising diverse locomotion and manipulation tasks. Project website: this https URL
Comments: ICML 2026 (Spotlight)
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.04525 [cs.RO]
(or arXiv:2605.04525v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.04525
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gireesh Nandiraju [view email] [v1] Wed, 6 May 2026 06:08:51 UTC (10,131 KB)
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