BURG-Toolkit: Robot Grasping Experiments in Simulation and the Real World

作     者：Rudorfer, Martin Suchi, Markus Sridharan, Mohan Vincze, Markus Leonardis, Aleš 

作者机构：The School of Computer Science The University of Birmingham United Kingdom The Automation and Control Institute TU Wien Austria 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Robotics 

摘      要：This paper presents BURG-Toolkit, a set of open-source tools for Benchmarking and Understanding Robotic Grasping. Our tools allow researchers to: (1) create virtual scenes for generating training data and performing grasping in simulation;(2) recreate the scene by arranging the corresponding objects accurately in the physical world for real robot experiments, supporting an analysis of the sim-to-real gap;and (3) share the scenes with other researchers to foster comparability and reproducibility of experimental results. We explain how to use our tools by describing some potential use cases. We further provide proof-of-concept experimental results quantifying the sim-to-real gap for robot grasping in some example scenes. The tools are available at: https://***/burg-toolkit/. Copyright © 2022, The Authors. All rights reserved.