解决长程规划中局部模式冲突导致的轨迹失真问题。
Refining Compositional Diffusion for Reliable Long-Horizon Planning
- 用自重构误差与重叠一致性引导采样，提升全局连贯性。
- 在OGBench多个任务上显著优于现有方法，成功率更高。
- 无需训练，可直接部署于预训练扩散模型，适合复杂任务规划。
组合扩散规划通过分数组合将重叠的短时段轨迹拼接生成长时程轨迹。然而，当局部规划分布具有多模态特性时，现有方法会因模式平均导致轨迹既不局部可行也不全局一致。本文提出无需训练的精炼组合扩散（RCD）方法，利用预训练扩散模型的自重构误差作为组合轨迹对数密度的代理，并引入重叠一致性项以确保段间一致性。实验表明，该联合引导机制使采样聚焦于高密度、低模式平均的优质轨迹。在包含运动控制、物体操作及像素观测的OGBench挑战性长时程任务中，RCD表现持续优于现有方法。
原文摘要 · Abstract (English)
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 consistency term that enforces consistency at segment boundaries. We show that the combined guidance concentrates sampling on high-density plans that mitigate mode-averaging. Experiments on challenging long-horizon tasks from OGBench, including locomotion, object manipulation, and pixel-based observations, demonstrate that RCD consistently outperforms existing methods.
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