发表机构
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI); Technical University of Munich (TUM); Tampere University; Aalto University（穆罕默德·本·扎耶德人工智能大学; 慕尼黑工业大学; 坦佩雷大学; 阿尔托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出分层规划框架，结合物理残差条件循环变分自编码器与降阶全身模型，实现人机布料协同运输中的障碍物规避，显著减少计算时间并降低布料变形。
AI 中文摘要
人机协同运输可变形物体需要在运动过程中预测物体变形，因为障碍物间隙取决于抓取点和未驱动的内部区域。我们提出了一种分层规划框架，该框架将学习到的布料模型与双臂移动操作器的降阶全身模型相结合。物理残差条件循环变分自编码器（p-cRVAE）通过学习对计算高效的线性化物理模型的残差修正，从抓取点观测预测完整布料构型，从而在40步规划范围内限制误差累积。预测的布料动力学被嵌入到模型预测路径积分（MPPI）规划器中，该规划器使用双臂移动操作器的降阶表示，保留了非完整基座约束和机械臂工作空间限制。随后，MPC层将采样的运动细化为平滑、可执行的全身控制参考。降阶公式实现了与完整17自由度模型相当的跟踪性能，同时计算时间减少了约80%。在四种协同运输场景和两种携带速度下，所提出的框架保持了布料与障碍物的间隙，而角点跟随基线会导致碰撞，同时全身细化将最终布料变形从0.93米减少到0.28米。
英文摘要
Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the grasp points and the unactuated interior. We present a hierarchical planning framework that combines a learned cloth model with a reduced-order whole-body model of a dual-arm mobile manipulator. A physics-residual conditional recurrent variational autoencoder (p-cRVAE) predicts the full cloth configuration from grasp-point observations by learning a residual correction to a computationally efficient linearized physics model, limiting error accumulation over 40-step planning horizon. The predicted cloth dynamics are embedded in a model predictive path integral (MPPI) planner using a reduced-order representation of a dual-arm mobile manipulator that preserves the non-holonomic base constraint and arm workspace limits. An MPC layer subsequently refines the sampled motion into smooth, executable references for whole-body control. The reduced-order formulation achieves tracking performance comparable to the full 17-DoF model while reducing computation time by approximately 80%. Across four co-transportation scenarios and two carrying speeds, the proposed framework maintains cloth-obstacle clearance where a corner-following baseline results in collisions, while whole-body refinement reduces final cloth deformation from 0.93\,m to 0.28\,m.
