This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on hand-engineering or learning generators for specific constraint types and then rejecting the value assignments when other constraints are violated. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP) derives global solutions to CCSPs by representing them as factor graphs and combining the energies of diffusion models trained to sample for individual constraint types. Diffusion-CCSP exhibits strong generalization to novel combinations of known constraints, and it can be integrated into a task and motion planner to devise long-horizon plans that include actions with both discrete and continuous parameters. Project site: https://diffusion-ccsp.github.io/

介绍一种学习解决连续约束满足问题（CCSP）的方法，在机器人推理和规划中应用。该方法通过将问题表示为因子图，并结合经过训练的扩散模型的能量来获得CCSP的全局解。Diffusion-CCSP对已知约束的新组合表现出强大的泛化能力，并可与任务和动作规划器集成，设计包含离散和连续参数动作的长期规划。

基于扩散的组合连续约束求解器