3D-CDRGP：面向3D开放世界中的跨设备机器人抓取策略
3D-CDRGP: Towards Cross-Device Robotic Grasping Policy in 3D Open World
AI总结：
本文率先探索3D开放世界中的跨设备（相机与机器人）抓取策略，构建两套不同制造商设备的真实抓取装置，提出SSGC-Seg模块实现类别无关的3D物体检测，并设计ScoreNet对检测结果评分以优先抓取高置信度物体，实验验证了策略的有效性和鲁棒性。
摘要：
鉴于设备的多样性和产品的升级换代，跨设备研究已成为亟待解决的关键问题。为此，我们率先探索了3D开放世界中的跨设备（相机与机器人）抓取策略。具体而言，我们构建了两个真实世界的抓取装置，采用了来自完全不同制造商的机械臂和相机。为了最小化来自不同相机的点云之间的域差异，我们采用聚类方法生成3D物体提议。然而，现有的聚类方法仅限于封闭集场景，这限制了机器人可抓取物体的类别，并使部署场景僵化。为了将这些方法扩展到开放世界环境，我们引入了SSGC-Seg模块，该模块能够实现类别无关的3D物体检测。所提出的模块通过分析每个点的SoftMax值，将原始的多类语义信息转换为二值语义线索——前景和背景，然后基于几何信息对前景点进行聚类以形成初始物体提议。此外，设计了ScoreNet‡来对每个检测结果进行评分，机械臂优先抓取置信度得分最高的物体。在两种不同类型的装置上的实验突出了我们的策略在跨设备机器人抓取研究中的有效性和鲁棒性。我们的代码在补充材料中提供，并将在论文被接收后发布。
英文摘要：
Given the diversity of devices and the product upgrades, cross-device research has become an urgent issue that needs to be tackled. To this end, we pioneer in probing the cross-device (cameras & robotics) grasping policy in the 3D open world. Specifically, we construct two real-world grasping setups, employing robotic arms and cameras from completely different manufacturers. To minimize domain differences in point clouds from diverse cameras, we adopt clustering methods to generate 3D object proposals. However, existing clustering methods are limited to closed-set scenarios, which confines the robotic graspable object categories and ossifies the deployment scenarios. To extend these methods to open-world settings, we introduce the SSGC-Seg module that enables category-agnostic 3D object detection. The proposed module transforms the original multi-class semantic information into binary semantic cues-foreground and background by analyzing the SoftMax value of each point, and then clusters the foreground points based on geometric information to form initial object proposals. Furthermore, ScoreNet‡ is designed to score each detection result, and the robotic arm prioritizes grasping the object with the highest confidence score. Experiments on two different types of setups highlight the effectiveness and robustness of our policy for cross-device robotics grasping research. Our code is provided in the supplementary and will be released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
