Compositional Diffusion-Based Continuous Constraint Solvers
Authors: name: Zhutian Yang, name: Jiayuan Mao, name: Yilun Du, name: Jiajun Wu, name: Joshua B. Tenenbaum, name: Tomás Lozano-Pérez, name: Leslie Pack Kaelbling

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/