用分层扩散-流模型规划长程任务，兼顾探索与实时性
HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
- 高阶扩散模型生成战略子目标，低阶流模型快速生成平滑轨迹
- 在4个家具组装任务中显著优于现有方法，真实世界任务成功率超90%
- 适合需要长程规划与实时执行的机器人任务，如复杂操作和移动
生成模型在长程稀疏奖励任务的行为规划上展现出潜力。然而，现有方法缺乏分层分解的合理框架，且因迭代去噪过程计算开销大，难以实现实时执行。本文提出分层扩散-流（HDFlow）规划框架，有效结合扩散模型与校正流模型的优势，克服单一范式生成规划器的局限。HDFlow采用高阶扩散规划器在学习的隐空间生成策略性子目标，利用扩散模型强大的探索能力；这些子目标引导低阶校正流规划器生成平滑密集的轨迹，充分发挥基于常微分方程（ODE）轨迹生成的速度与效率。我们在仿真与真实世界中的四个挑战性家具组装任务上评估了HDFlow，结果显著优于当前最先进方法。此外，还在两个涵盖多样化运动与操作任务的长程基准上验证了方法的泛化能力。
原文摘要 · Abstract (English)
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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