面向布料操作的带生成式状态估计的扩散动力学模型
Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation
AI总结：
针对布料操作中状态估计与动力学建模的难题，提出基于transformer扩散模型的生成式方法，大幅降低长时预测误差，结合模型预测控制实现真实机器人布料折叠。
摘要：
布料操作因动力学高度复杂、自由度近乎无穷且频繁发生自遮挡而极具挑战性，这使得状态估计与动力学建模均难度大增。受生成式模型近期进展的启发，我们假设这些高表达能力的模型能够从数据中有效捕捉复杂的布料构型与形变模式。为此，我们提出一种同时用于感知与动力学建模的基于扩散的生成式方法。具体而言，我们将状态估计定义为从部分观测重建完整布料状态，将动力学建模定义为根据当前状态与机器人动作预测未来状态。借助基于transformer的扩散模型，我们的方法实现了准确的状态重建，且长时程动力学预测误差相比现有方法降低了一个数量级。我们将该动力学模型与模型预测控制（MPC）集成，验证了该框架可在真实机器人系统上完成有效的布料折叠，证明了生成式模型在部分可观测、复杂动力学条件下处理可变形物体操作的潜力。
英文摘要：
Cloth manipulation is challenging due to its highly complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions, which complicate both state estimation and dynamics modeling. Inspired by recent advances in generative models, we hypothesize that these expressive models can effectively capture intricate cloth configurations and deformation patterns from data. Therefore, we propose a diffusion-based generative approach for both perception and dynamics modeling. Specifically, we formulate state estimation as reconstructing full cloth states from partial observations and dynamics modeling as predicting future states given the current state and robot actions. Leveraging a transformer-based diffusion model, our method achieves accurate state reconstruction and reduces long-horizon dynamics prediction errors by an order of magnitude compared to prior approaches. We integrate our dynamics models with model predictive control and show that our framework enables effective cloth folding on real robotic systems, demonstrating the potential of generative models for deformable object manipulation under partial observability and complex dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
