Compositional Diffusion-Based Continuous Constraint Solvers
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/
64f82b48b0f25f3fe3a1cb5f
Zhutian Yang
64f82b48b0f25f3fe3a1cb60
Jiayuan Mao
64f82b48b0f25f3fe3a1cb61
Yilun Du
64f82b48b0f25f3fe3a1cb62
Jiajun Wu
claimed_verified
2023-12-21T09:32:19.425Z
647b7b8345616c3de3627863
/avatars/8196364bffed71086ba9a0a4a62027d3.svg
Jiajun Wu
user
jiajunwu
64f82b48b0f25f3fe3a1cb63
Joshua B. Tenenbaum
64f82b48b0f25f3fe3a1cb64
Tomás Lozano-Pérez
64f82b48b0f25f3fe3a1cb65
Leslie Pack Kaelbling
Compositional Diffusion-Based Continuous Constraint Solvers
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/
compositional diffusion continuous constraint solver (Diffusion-CCSP) continuous constraint satisfaction problems (CCSP) robotic reasoning and planning CNN diffusion models perspective graphs global solutions constraint types task and motion planner long-horizon plans discrete and continuous parameters