The Grasp Reset Mechanism: An Automated Apparatus for Conducting Grasping Trials
<|reference_start|>The Grasp Reset Mechanism: An Automated Apparatus for Conducting Grasping Trials: 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.<|reference_end|>
@article{dufrene2024the,
    title={The Grasp Reset Mechanism: An Automated Apparatus for Conducting
  Grasping Trials},
    author={Kyle DuFrene, Keegan Nave, Joshua Campbell, Ravi Balasubramanian, and
  Cindy Grimm},
    journal={arXiv preprint arXiv:2402.18650},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18650},
    primaryClass={cs.RO}
}