Compositional Diffusion-Based Continuous Constraint Solvers

• URL: http://arxiv.org/abs/2309.00966v1
• Date: Sat, 2 Sep 2023 15:20:36 GMT
• Title: Compositional Diffusion-Based Continuous Constraint Solvers
• Authors: Zhutian Yang, Jiayuan Mao, Yilun Du, Jiajun Wu, Joshua B. Tenenbaum, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling
• Abstract summary: This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP), derives global solutions to CCSPs.
• Score: 98.1702285470628
• License: http://creativecommons.org/licenses/by/4.0/
• 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/

Related papers

• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics. This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• Generalizing Constraint Models in Constraint Acquisition [6.305123652677644]
  Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. Most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constraints to the parameterized constraint specifications of the problem. We propose GenCon, a novel approach to learn parameterized constraint models capable of modeling varying instances of the same problem.
  arXiv  Detail & Related papers  (2024-12-19T15:31:29Z)
• Diffusion Predictive Control with Constraints [51.91057765703533]
  Diffusion predictive control with constraints (DPCC) An algorithm for diffusion-based control with explicit state and action constraints that can deviate from those in the training data. We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints while maintaining performance on the learned control task.
  arXiv  Detail & Related papers  (2024-12-12T15:10:22Z)
• Rectified Diffusion Guidance for Conditional Generation [62.00207951161297]
  We revisit the theory behind CFG and rigorously confirm that the improper configuration of the combination coefficients (i.e., the widely used summing-to-one version) brings about expectation shift of the generative distribution. We propose ReCFG with a relaxation on the guidance coefficients such that denoising with ReCFG strictly aligns with the diffusion theory. That way the rectified coefficients can be readily pre-computed via traversing the observed data, leaving the sampling speed barely affected.
  arXiv  Detail & Related papers  (2024-10-24T13:41:32Z)
• Spatio-temporal Diffusion Point Processes [23.74522530140201]
  patio-temporal point process (STPP) is a collection of events accompanied with time and space. The failure to model the joint distribution leads to limited capacities in characterizing the pasthua-temporal interactions given events. We propose a novel parameterization framework, which learns complex spatial-temporal joint distributions. Our framework outperforms the state-of-the-art baselines remarkably, with an average improvement over 50%.
  arXiv  Detail & Related papers  (2023-05-21T08:53:00Z)
• Switchable Representation Learning Framework with Self-compatibility [50.48336074436792]
  We propose a Switchable representation learning Framework with Self-Compatibility (SFSC) SFSC generates a series of compatible sub-models with different capacities through one training process. SFSC achieves state-of-the-art performance on the evaluated datasets.
  arXiv  Detail & Related papers  (2022-06-16T16:46:32Z)
• Handling Hard Affine SDP Shape Constraints in RKHSs [3.8073142980733]
  We propose a unified and modular convex optimization framework to encode hard affine SDP constraints on function derivatives. We prove the consistency of the proposed scheme and that of its adaptive variant, leveraging geometric properties of vRKHSs.
  arXiv  Detail & Related papers  (2021-01-05T14:08:58Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.