Compositional Diffusion-Based Continuous Constraint Solvers
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/
Cite
@article{arxiv.2309.00966,
title = {Compositional Diffusion-Based Continuous Constraint Solvers},
author = {Zhutian Yang and Jiayuan Mao and Yilun Du and Jiajun Wu and Joshua B. Tenenbaum and Tomás Lozano-Pérez and Leslie Pack Kaelbling},
journal= {arXiv preprint arXiv:2309.00966},
year = {2023}
}