| CARVIEW |
Abstract
Long-horizon manipulation tasks represent a significant challenge in robotics, demanding both strategic, high-level reasoning and fast, precise, low-level control. While recent advances in generative models have shown promise in generating behavior plans for long-horizon tasks, 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. 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. This hybrid approach synergistically combines the strengths of both models to overcome the limitations of single-paradigm generative planners, enabling robust and efficient long-horizon planning. We evaluate HDFlow on four challenging furniture assembly tasks in both simulation and real-world, where it significantly outperforms state-of-the-art methods.
Key Insight
The iterative denoising process of diffusion models is computationally expensive, making them ill-suited for the fast, low-level control required for real-time robotic interaction. Applying diffusion models naively at all levels of a hierarchy inherits this critical drawback, creating a bottleneck at the trajectory generation stage. This raises a fundamental question: Is a single generative modeling paradigm optimal for all levels of a planning hierarchy?
We empirically show that the answer is no. The requirements for high-level strategic planning are fundamentally different from those of low-level trajectory generation. High-level planning demands exploration and multi-modal diversity to discover viable sequences of subgoals. In contrast, low-level planning demands speed, precision, and deterministic execution to translate a chosen subgoal into a smooth, dense trajectory.
HDFlow
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. Our framework consists of two main stages: World Model Learning (left), where observations are encoded into a structured latent space, and Hierarchical Planner Training (right). The latter involves a High-Level diffusion planner generating sparse strategic subgoals with EBM guidance, and a Low-Level rectified flow planner synthesizing dense trajectories between subgoals using an ODE solver.
