HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks

• URL: http://arxiv.org/abs/2605.04525v1
• Date: Wed, 06 May 2026 06:08:51 GMT
• Title: HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
• Abstract summary: We introduce Hierarchical Diffusion-Flow (HDFlow), a novel hierarchical planning framework.<n>HDFlow optimally leverages the strengths of diffusion and rectified flow models to overcome the limitations of single-paradigm generative planners.<n>We evaluate HDFlow on four challenging furniture assembly tasks in both simulation and real-world, where it significantly outperforms state-of-the-art methods.
• Score: 11.988273046923782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://hdflow-page.github.io/

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