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arXiv · 2309.00966

Compositional Diffusion-Based Continuous Constraint Solvers

Zhutian Yang·Jiayuan Mao·Yilun Du·Jiajun Wu·Joshua B. Tenenbaum·Tomás Lozano-Pérez·Leslie Pack Kaelbling
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Abstract

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/

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Zhutian Yang, Jiayuan Mao, Yilun Du, Jiajun Wu, Joshua B. Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling. 2023-09-02. Compositional Diffusion-Based Continuous Constraint Solvers. https://arxiv.org/abs/2309.00966

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