Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy

2025-05-23

AI Summary

Controlling high-degree-of-freedom, underactuated deformable objects (e.g., ropes) in 3D space toward dynamic, goal-directed tasks remains challenging due to complex nonlinear dynamics and limited observability. Method: We propose a dynamics-aware diffusion policy (DIDP), built upon the first efficient, reduced-order dynamics-based 3D deformable object simulation framework and benchmark. DIDP integrates inverse dynamics learning with physical constraints and introduces a test-time adaptive diffusion mechanism—eliminating reliance on large-scale real-world data or static-motion assumptions. Contributions/Results: (1) First demonstration of controllable, reduced-order-dynamics-driven 3D deformable object simulation; (2) A physics-guided diffusion strategy that significantly improves end-effector localization accuracy and environmental robustness; (3) Strong generalization from sparse imitation pretraining, effectively mitigating the scarcity of real-world demonstration data. Experiments validate superior performance in dynamic manipulation tasks under diverse environmental perturbations and geometric configurations.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Goal-conditioned dynamic manipulation is inherently challenging due to complex system dynamics and stringent task constraints, particularly in deformable object scenarios characterized by high degrees of freedom and underactuation. Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks. In this work, we explore 3D goal-conditioned rope manipulation as a representative challenge. To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics, which enables compact state representation and facilitates efficient policy learning. Building on this, we propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation. First, we design a diffusion policy that learns inverse dynamics within the reduced-order space, enabling imitation learning to move beyond na""ive data fitting and capture the underlying physical structure. Second, we propose a physics-informed test-time adaptation scheme that imposes kinematic boundary conditions and structured dynamics priors on the diffusion process, ensuring consistency and reliability in manipulation execution. Extensive experiments validate the proposed approach, demonstrating strong performance in terms of accuracy and robustness in the learned policy.

Problem

• Addressing 3D deformable object manipulation challenges
• Overcoming data scarcity with simulation and benchmark
• Integrating physics into learning for reliable execution

Innovation

• Novel simulation framework with reduced-order dynamics
• Dynamics Informed Diffusion Policy (DIDP) integration
• Physics-informed test-time adaptation scheme