Refining Compositional Diffusion for Reliable Long-Horizon Planning
Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However, when local plan distributions are multimodal, existing compositional methods suffer from mode-averaging, where averaging incompatible local modes leads to plans that are neither locally feasible nor globally coherent. We propose Refining Compositional Diffusion (RCD), a training-free guidance method that steers compositional sampling toward high-density, globally coherent plans. RCD leverages the self-reconstruction error of a pretrained diffusion model as a proxy for the log-density of composed plans, combined with an overlap co...
| Authors | Kyowoon Lee, Yunhao Luo, Anh Tong et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 May 2026 |
| Last updated at source | 10 October 2026 |
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