双臂机器人自适应抓取布袋，无需预先知道袋子特性。
BagIt! An Adaptive Dual-Arm Manipulation of Fabric Bags for Object Bagging
- 根据视觉反馈动态调整动作，实时估计袋口位置
- 在多种物体上实现精准稳定抓取，成功率高
- 适合工业自动化场景，尤其适合柔性物体操作
工业场景中常见的装袋任务因布袋的可变形性和不可预测性而极具挑战。本文提出一种基于自适应兴趣结构（SOI）策略的双机械臂自动装袋系统。系统通过实时视觉反馈动态调整操作，无需预知袋体属性。框架融合高斯混合模型（GMM）估计SOI状态、优化算法生成SOI、基于约束双向快速扩展随机树（CBiRRT）进行运动规划，并采用模型预测控制（MPC）实现双臂协同。大量实验验证了系统在多种物体上的精确与鲁棒装袋能力，展现出良好的适应性。本工作为机器人柔性物体操作（DOM）提供了新方案，尤其适用于自动化装袋任务。视频见 https://youtu.be/6JWjCOeTGiQ。
原文摘要 · Abstract (English)
Bagging tasks, commonly found in industrial scenarios, are challenging considering deformable bags' complicated and unpredictable nature. This paper presents an automated bagging system from the proposed adaptive Structure-of-Interest (SOI) manipulation strategy for dual robot arms. The system dynamically adjusts its actions based on real-time visual feedback, removing the need for pre-existing knowledge of bag properties. Our framework incorporates Gaussian Mixture Models (GMM) for estimating SOI states, optimization techniques for SOI generation, motion planning via Constrained Bidirectional Rapidly-exploring Random Tree (CBiRRT), and dual-arm coordination using Model Predictive Control (MPC). Extensive experiments validate the capability of our system to perform precise and robust bagging across various objects, showcasing its adaptability. This work offers a new solution for robotic deformable object manipulation (DOM), particularly in automated bagging tasks. Video of this work is available at https://youtu.be/6JWjCOeTGiQ.
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