多様性と再現性を目指したロボット把持用データセットEGAD
EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
ロボットの視覚的把持検出アルゴリズムの学習・評価用に、形状が多様な2000以上の物体を生成したデータセットEGADを構築し、再現可能な評価のための3Dプリント可能な49物体も提供した。
著者: Douglas Morrison, Peter Corke, Jürgen Leitner
分類: cs.RO
原文アブストラクト
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of complexity and difficulty. The dataset, code and videos can be found at https://dougsm.github.io/egad/
