用引导搜索解决生成模型长程规划中局部模式冲突问题
Compositional Diffusion with Guided Search for Long-Horizon Planning
- 在扩散模型去噪过程内嵌搜索，通过种群采样探索局部模式组合
- 在7个机器人操作任务上达到最优性能，无需长时序训练数据
- 适用于跨领域长视频、全景图生成，实现局部到全局一致
生成模型已成为规划的强大工具，其中组合式方法通过组合局部模块化生成模型，为建模长程任务分布提供了独特前景。该范式涵盖多步操作规划、全景图像合成及长视频生成等多个领域。然而，组合式生成模型面临关键挑战：当局部分布具有多模态性时，现有组合方法会平均不兼容的模式，导致生成的计划既不可行也缺乏全局一致性。我们提出组合扩散引导搜索（CDGS），通过将搜索直接嵌入扩散去噪过程，解决模式平均问题。该方法通过种群采样探索局部模式的多样化组合，利用似然过滤剔除不可行候选，通过重采样机制在重叠段间强制全局一致性。CDGS在七个机器人操作任务上达到基准性能，优于缺乏组合性的基线或依赖长时序训练数据的方法。该方法具备跨域泛化能力，支持通过有效局部到全局信息传递生成连贯的文本引导全景图与长视频。
原文摘要 · Abstract (English)
Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are multimodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coherent. We propose Compositional Diffusion with Guided Search (CDGS), which addresses this mode averaging problem by embedding search directly within the diffusion denoising process. Our method explores diverse combinations of local modes through population-based sampling, prunes infeasible candidates using likelihood-based filtering, and enforces global consistency through iterative resampling between overlapping segments. CDGS matches oracle performance on seven robot manipulation tasks, outperforming baselines that lack compositionality or require long-horizon training data. The approach generalizes across domains, enabling coherent text-guided panoramic images and long videos through effective local-to-global message passing. More details: https://cdgsearch.github.io/
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