混雑環境におけるアモーダル単視点単発SE(3)把持検出
S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes
単一視点の深度画像から混雑環境での6自由度把持を学習ベースで検出する単発ネットワークを提案し、合成データと実環境で従来手法を大きく上回る性能を示した。
著者: Yuzhe Qin, Rui Chen, Hao Zhu, Meng Song, Jing Xu, Hao Su
分類: cs.RO, cs.CV
原文アブストラクト
Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform state-of-the-arts by a large margin.
