发表机构
Interdisciplinary Program in AI, Seoul National University; Artificial Intelligence Institute, Seoul National University; Department of Computer Science, Seoul National University（首尔国立大学人工智能跨学科项目; 首尔国立大学人工智能研究所; 首尔国立大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对非结构化环境中无序抓取难题，提出Seg2Grasp模块化方法，经分割、抓取、分类三步流程，利用多种技术确定吸力点与识别物体，实际实验证明该方法在成功率和适应性上优于现有方法，是工业自动无序抓取有力工具。
AI 中文摘要
当前严重依赖端到端学习的无序抓取方法，在面对非结构化环境中不熟悉或复杂物体时常常失败。为克服这些限制，我们引入了Seg2Grasp，这是一种为动态和杂乱无序场景中的稳健吸力抓取设计的模块化流程。Seg2Grasp基于分割、抓取和分类三步构建。分割模块采用基于Transformer的模型从RGB-D图像生成与类别无关的物体掩码；抓取模块利用表面法线和掩码提议确定最佳吸力点；分类模块利用微调的开放词汇Mask-CLIP进行精确物体识别。实际机器人实验表明，Seg2Grasp在成功率和适应性方面优于现有方法，成为工业环境中自动无序抓取的有力工具。
英文摘要
Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments. To overcome these limitations, we introduce Seg2Grasp, a modular pipeline designed for robust suction grasping in dynamic and cluttered bin scenarios. Seg2Grasp is built on a three-step process: Segmentation, Grasping, and Classification. The Segmentation module employs a Transformer-based model to generate class-agnostic object masks from RGB-D images, ensuring accurate detection across various conditions. The Grasping module uses surface normals and mask proposals to determine the optimal suction points, enhancing grasp success. Finally, the Classification module leverages fine-tuned open-vocabulary Mask-CLIP for precise object identification, enabling versatile handling of diverse objects. Real-world robotic experiments demonstrate that Seg2Grasp outperforms existing methods in success rates and adaptability, establishing it as a powerful tool for automated bin picking in industrial settings.
Comments7 pages, 6 figures, 2 tables. Published in the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
Journal ref2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024, pp. 2921-2927
DOI：10.1109/IROS58592.2024.10801644
