BURG-Toolkit:シミュレーションと実世界でのロボット把持実験を支援するツールキット
BURG-Toolkit: Robot Grasping Experiments in Simulation and the Real World
ロボット把持のベンチマーク用オープンソースツール群を提案し、仮想シーン生成・実世界での再現・シーン共有を可能にするとともに、sim-to-realギャップを定量化する実験例を示した。
著者: Martin Rudorfer, Markus Suchi, Mohan Sridharan, Markus Vincze, Aleš Leonardis
分類: cs.RO
原文アブストラクト
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://mrudorfer.github.io/burg-toolkit/
関連論文
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- アクション画像表現:実世界データなしで学習するスケーラブルな深層把持ポリシー把持/sim2real
