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.

翻译：推进机器人抓取与操作能力需要在大规模抓取数据上测试算法和/或训练学习模型。为实现更高级的抓取目标，我们提出了抓取复位机制（GRM），这是一种完全自动化的装置，用于执行大规模抓取试验。GRM实现了抓取环境复位过程的自动化，能够重复地将物体放置在固定位置和可控的一维朝向。该装置还能自动收集数据并在多个物体之间切换，无需人工干预即可完成鲁棒的数据集采集。我们还提供了一种标准化的状态机控制接口，可轻松集成大多数机械臂。除物理设计与配套软件外，我们附带包含1,020次抓取的数据集。该数据集由Kinova Gen3机械臂和Robotiq 2F-85自适应夹爪生成，旨在支持学习模型训练并展示GRM的能力。数据集涵盖了四个物体在不同朝向上的多种抓取试验，每次试验均提供机械臂状态、物体位姿、视频及抓取成功数据。