基于组合扩散的连续约束求解器

摘要

本文介绍了一种学习解决连续约束满足问题（CCSP）的方法，适用于机器人推理和规划。先前的方法主要依赖手工设计或学习生成器来处理特定约束类型，然后在违反其他约束时拒绝数值分配。相比之下，我们的模型，即组合扩散连续约束求解器（Diffusion-CCSP），通过将CCSP表示为因子图，并结合训练用于对各个约束类型进行采样的扩散模型的能量，来推导全局解。Diffusion-CCSP对已知约束的新组合表现出强大的泛化能力，并可集成到任务和运动规划器中，设计包含离散和连续参数动作的长视程计划。项目网站：https://diffusion-ccsp.github.io/

English

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/

基于组合扩散的连续约束求解器

Compositional Diffusion-Based Continuous Constraint Solvers

摘要

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