基於組合擴散的連續約束求解器

摘要

本文介紹了一種學習解決機器人推理和規劃中連續約束滿足問題（CCSP）的方法。先前的方法主要依賴手工設計或學習產生器來處理特定約束類型，然後在違反其他約束時拒絕值分配。相比之下，我們的模型，即組合擴散連續約束求解器（Diffusion-CCSP），通過將其表示為因子圖並結合訓練用於對單個約束類型進行抽樣的擴散模型的能量，來導出CCSP的全局解。Diffusion-CCSP對已知約束的新組合表現出強大的泛化能力，並且可以集成到任務和運動規劃器中，制定包含具有離散和連續參數的動作的長視程計劃。項目網站：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/

基於組合擴散的連續約束求解器

Compositional Diffusion-Based Continuous Constraint Solvers

摘要

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