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/
A compositional diffusion model learns to solve continuous constraint satisfaction problems by combining energies of diffusion models for individual constraints, enabling generalization and integration into task and motion planning.
diffusion models factor graphs continuous constraint satisfaction problems compositional diffusion task and motion planner generalization