HCDiff: Hierarchical Latent Constraint-Projected Diffusion Framework for Deformable Linear Objects Manipulation in Cluttered Environments

Manipulating Deformable Linear Objects (DLOs) in cluttered environments is challenging due to their high dimensionality and complex constraints. In this paper, we propose a Hierarchical Constraint-projected Diffusion (HCDiff) framework that decomposes the problem into global constraint-aware planning and local robust control through a coarse-to-fine generative process. At the high level, we propose a Latent Control Barrier Function (LCBF) projection that is integrated directly into the score-based denoising process of a diffusion planner. By projecting the gradient flow within the latent manifold learned by a Graph AutoEncoder (GAE), our method biases the generation of subgoals toward collision-free and physically feasible configurations. At the low level, we develop a Goal-conditioned Diffusion Policy (GDP) for DLO manipulation that is trained solely on unstructured, task-agnostic play data. By offloading local control to this task-agnostic policy, our high-level planner only requires a small scale of task-specific human demonstrations, thereby significantly reducing the overall dependency on expensive expert data. Furthermore, we employ a dual GDP control strategy to enhance control robustness. Comprehensive experiments in 2D and 3D demonstrate that HCDiff outperforms multiple baselines in terms of task success rate and planning efficiency. Crucially, the framework exhibits superior adaptability, successfully navigating environments with modified obstacle geometries beyond the training distribution.