# 面向障碍感知人机布料协同搬运的物理残差动力学与降阶全身规划

> 原文链接：https://robotics.hellobike.com/papers/2026-10-05-2610-06641
> 频道：papers | 更新日期：2026-10-05 | 标签：arxiv, cs.RO
> 核心摘要：针对可变形布料协同搬运避障，提出结合p-cRVAE布料预测与降阶全身模型的层级规划，并在四场景两速度验证避碰效果。

# 面向障碍感知人机布料协同搬运的物理残差动力学与降阶全身规划

::: v-pre
> 原文链接：[Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation](https://arxiv.org/abs/2610.06641)（arXiv 2610.06641 · 发布日期：2026-10-05）

**Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation**

针对可变形布料协同搬运避障，提出结合p-cRVAE布料预测与降阶全身模型的层级规划，并在四场景两速度验证避碰效果。

## 核心要点

- 问题在于搬运中需预测布料未驱动内部形变，因为越障间隙取决于抓取点和布料整体状态。
- 方法用p-cRVAE在高效线性化物理模型上学习残差，由抓取点观测预测完整布料配置，并嵌入MPPI规划。
- 降阶双臂移动操作器模型保留非完整底盘约束和手臂工作空间限制，随后由MPC细化为平滑可执行参考。
- 降阶模型跟踪性能接近完整17-DoF模型且计算时间约降80%；四场景两速度下相较基线保持越障间隙并减少最终形变。

**验证范围与局限**：验证范围为摘要所述四个协同搬运场景和两种搬运速度，摘要未说明是否包含真实机器人实验。

## 英文摘要 (Abstract)

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.

作者：Moein Forouhar, Kosar Behnia, Anirvan Dutta, Hamid Sadeghian, Ville Kyrki, Sami Haddadin, Gökhan Alcan, Eckehard G. Steinbach
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## 原始参考来源 (Sources)

- [arXiv 2610.06641：Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation](https://arxiv.org/abs/2610.06641)