用扩散模型构建可组合的规划图，实现复杂双臂操作与泛化。
Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph
- 将任务建模为时空因子图，每个因子由扩散模型实现
- 通过双向消息传递生成长时序可行计划，支持新物体组合
- 适用于未见过的多步操作任务，具强泛化能力
学习多步骤、多机械臂任务的规划极具挑战性，源于庞大的搜索空间和复杂的约束满足问题。本文提出生成因子链（Generative Factor Chaining, GFC），一种可组合的生成式规划模型。GFC 将规划问题表示为时空因子图，其中节点代表场景中的物体和机器人，空间因子捕获节点间有效关系的分布，时间因子表示技能转换的分布。每个因子以模块化扩散模型实现，并在推理时组合，通过双向消息传递生成长时序可行计划。实验表明，GFC 能解决复杂的双臂操作任务，并对包含新物体与新约束的未见规划任务展现出强大泛化能力。
原文摘要 · Abstract (English)
Learning to plan for multi-step, multi-manipulator tasks is notoriously difficult because of the large search space and the complex constraint satisfaction problems. We present Generative Factor Chaining~(GFC), a composable generative model for planning. GFC represents a planning problem as a spatial-temporal factor graph, where nodes represent objects and robots in the scene, spatial factors capture the distributions of valid relationships among nodes, and temporal factors represent the distributions of skill transitions. Each factor is implemented as a modular diffusion model, which are composed during inference to generate feasible long-horizon plans through bi-directional message passing. We show that GFC can solve complex bimanual manipulation tasks and exhibits strong generalization to unseen planning tasks with novel combinations of objects and constraints. More details can be found at: https://generative-fc.github.io/
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