Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation

• URL: http://arxiv.org/abs/2508.19199v1
• Date: Tue, 26 Aug 2025 17:03:39 GMT
• Title: Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation
• Authors: Alex LaGrassa, Zixuan Huang, Dmitry Berenson, Oliver Kroemer,
• Abstract summary: This paper introduces a method that automatically generates task-specific, spatially adaptive dynamics models.<n>On a tree-manipulation task, our method doubles planning speed with only a small decrease in task performance over using a full-resolution model.
• Score: 24.086752654743957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient planning in high-dimensional spaces, such as those involving deformable objects, requires computationally tractable yet sufficiently expressive dynamics models. This paper introduces a method that automatically generates task-specific, spatially adaptive dynamics models by learning which regions of the object require high-resolution modeling to achieve good task performance for a given planning query. Task performance depends on the complex interplay between the dynamics model, world dynamics, control, and task requirements. Our proposed diffusion-based model generator predicts per-region model resolutions based on start and goal pointclouds that define the planning query. To efficiently collect the data for learning this mapping, a two-stage process optimizes resolution using predictive dynamics as a prior before directly optimizing using closed-loop performance. On a tree-manipulation task, our method doubles planning speed with only a small decrease in task performance over using a full-resolution model. This approach informs a path towards using previous planning and control data to generate computationally efficient yet sufficiently expressive dynamics models for new tasks.

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