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

2025-08-26

AI Summary

To address the trade-off between computational efficiency and modeling expressiveness in dynamic modeling of high-dimensional deformable objects, this paper proposes a task-driven, spatially adaptive method for automatic dynamic model generation. The method introduces diffusion models to predict local modeling resolution at critical regions from point-cloud-based planning queries—a novel application in this domain. It further designs a two-stage joint optimization framework that unifies predicted dynamics priors with closed-loop control performance feedback, enabling co-optimization of spatial resolution distribution and dynamical parameters. Integrating point-cloud perception, differentiable physics simulation, and closed-loop simulation data collection, the approach achieves a 2× speedup over full-resolution modeling on tree-like object manipulation tasks, with less than 3% degradation in task success rate. This substantially enhances both planning efficiency and practical deployability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Generates task-specific adaptive dynamics models for deformable objects
• Determines optimal resolution regions for efficient planning queries
• Balances computational efficiency with task performance requirements

Innovation

• Generates task-specific adaptive resolution models
• Uses diffusion-based predictor for region resolutions
• Two-stage data optimization with predictive prior