Compositional Diffusion-Based Continuous Constraint Solvers

• URL: http://arxiv.org/abs/2309.00966v1
• Date: Sat, 2 Sep 2023 15:20:36 GMT
• Title: Compositional Diffusion-Based Continuous Constraint Solvers
• Authors: Zhutian Yang, Jiayuan Mao, Yilun Du, Jiajun Wu, Joshua B. Tenenbaum, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling
• Abstract summary: This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP), derives global solutions to CCSPs.
• Score: 98.1702285470628
• License: http://creativecommons.org/licenses/by/4.0/
• 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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