发表机构
University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对可回收废物机器人分拣的挑战，提出一种免训练的变形无菌纸箱吸盘抓取系统，结合视觉语言模型、SAM2与几何表面评分方法，在真实机器人实验中取得了良好的抓取与检索效果。
AI 中文摘要
可回收废物的机器人分拣因目标物体的可变形性和几何不一致性而颇具挑战。本文提出一种用于分拣变形无菌饮料纸箱的免训练吸盘抓取系统，将目标识别与抓取点选择解耦。采用开放词汇视觉语言模型通过文本提示检测纸箱，SAM2将每个检测结果细化为实例掩码，几何评分方法结合表面平坦度与法向对齐度选择吸盘点。对比三种几何方法：k近邻主成分分析、Sobel叉积和RANSAC平面拟合。在真实机器人上针对三种变形程度和35个杂乱场景进行评估，单物体抓取成功率达88.2%，杂乱环境中端到端检索成功率为72.6%。
英文摘要
Robotic sorting of recyclable waste is challenging due to the deformable and geometrically inconsistent nature of target objects. We present a training-free suction grasping system for sorting deformed aseptic beverage cartons, decoupling target identification from grasp-point selection. An open-vocabulary vision-language model detects cartons from a text prompt, SAM2 refines each detection into an instance mask, and a geometric scoring method selects the suction point by combining surface flatness with normal alignment. Three geometric methods are compared: k-nearest-neighbour PCA, Sobel cross-product, and RANSAC plane fitting. Evaluated on a real robot across three deformation levels and 35 cluttered scenes, single-object grasp success reaches 88.2% and end-to-end retrieval in clutter is 72.6%.
Comments6 pages, ICCAS 2026 preprint
