抓取重置机制：一种用于执行抓取试验的自动化装置
The Grasp Reset Mechanism: An Automated Apparatus for Conducting Grasping Trials
AI总结：
本文提出全自动化的 Grasp Reset Mechanism（GRM），可重置环境、切换物体并采集多模态抓取数据，同时提供标准化控制接口及含1,020次抓取的数据集。
摘要：
推进机器人抓取与操作需要具备在大量抓取上测试算法和/或训练学习模型的能力。为实现更先进的抓取，我们提出了 Grasp Reset Mechanism（GRM，抓取重置机制），这是一种用于执行大规模抓取试验的全自动化装置。GRM 使抓取环境的重置过程实现自动化，能够可重复地将物体放置在固定位置，并提供可控的一维朝向。它还会收集数据并在多个物体之间切换，从而在没有人工干预的情况下实现稳健的数据集采集。我们还提出了用于控制的标准化状态机接口，该接口允许以最少的工作量集成大多数机械臂。除了物理设计和相应软件之外，我们还提供了一个包含 1,020 次抓取的数据集。这些抓取由 Kinova Gen3 机械臂和 Robotiq 2F-85 自适应夹爪完成，用于训练学习模型并展示 GRM 的能力。该数据集包含针对四个物体以及多种朝向执行的一系列抓取。每次试验都提供机械臂状态、物体位姿、视频和抓取成功数据。
英文摘要：
Advancing robotic grasping and manipulation requires the ability to test algorithms and/or train learning models on large numbers of grasps. Towards the goal of more advanced grasping, we present the Grasp Reset Mechanism (GRM), a fully automated apparatus for conducting large-scale grasping trials. The GRM automates the process of resetting a grasping environment, repeatably placing an object in a fixed location and controllable 1-D orientation. It also collects data and swaps between multiple objects enabling robust dataset collection with no human intervention. We also present a standardized state machine interface for control, which allows for integration of most manipulators with minimal effort. In addition to the physical design and corresponding software, we include a dataset of 1,020 grasps. The grasps were created with a Kinova Gen3 robot arm and Robotiq 2F-85 Adaptive Gripper to enable training of learning models and to demonstrate the capabilities of the GRM. The dataset includes ranges of grasps conducted across four objects and a variety of orientations. Manipulator states, object pose, video, and grasp success data are provided for every trial.
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。
