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.
@article{arxiv.2402.18650,
title = {The Grasp Reset Mechanism: An Automated Apparatus for Conducting Grasping Trials},
author = {Kyle DuFrene and Keegan Nave and Joshua Campbell and Ravi Balasubramanian and Cindy Grimm},
journal= {arXiv preprint arXiv:2402.18650},
year = {2024}
}
Comments
Accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA2024)