title: Compositional Diffusion-Based Continuous Constraint Solvers
summary: 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/
paper_authors._id: 64f82b48b0f25f3fe3a1cb5f
paper_authors.name: Zhutian Yang
paper_authors._id: 64f82b48b0f25f3fe3a1cb60
paper_authors.name: Jiayuan Mao
paper_authors._id: 64f82b48b0f25f3fe3a1cb61
paper_authors.name: Yilun Du
paper_authors._id: 64f82b48b0f25f3fe3a1cb62
paper_authors.name: Jiajun Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-21T09:32:19.425Z
paper_authors._id: 647b7b8345616c3de3627863
paper_authors.avatarUrl: /avatars/8196364bffed71086ba9a0a4a62027d3.svg
paper_authors.fullname: Jiajun Wu
paper_authors.type: user
paper_authors.user: jiajunwu
paper_authors._id: 64f82b48b0f25f3fe3a1cb63
paper_authors.name: Joshua B. Tenenbaum
paper_authors._id: 64f82b48b0f25f3fe3a1cb64
paper_authors.name: Tomás Lozano-Pérez
paper_authors._id: 64f82b48b0f25f3fe3a1cb65
paper_authors.name: Leslie Pack Kaelbling
paper_title: Compositional Diffusion-Based Continuous Constraint Solvers
paper_summary: 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/
paper_ai_keywords: 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