注意機構に基づく視覚解析による多指ロボットハンドの高速把持計画
Attention based visual analysis for fast grasp planning with multi-fingered robotic hand
視覚的注意モデルで物体の注目領域を特定し、深層畳み込みニューラネットで把持タイプと把持点を検出することで、多指ハンドの把持計画を高速化・安定化させるフレームワークを提案した論文。未知物体や混雑環境にも対応できる。
著者: Zhen Deng, Ge Gao, Simone Frintrop, Jianwei Zhang
分類: cs.RO
原文アブストラクト
We present an attention based visual analysis framework to compute grasp-relevant information in order to guide grasp planning using a multi-fingered robotic hand. Our approach uses a computational visual attention model to locate regions of interest in a scene, and uses a deep convolutional neural network to detect grasp type and point for a sub-region of the object presented in a region of interest. We demonstrate the proposed framework in object grasping tasks, in which the information generated from the proposed framework is used as prior information to guide the grasp planning. Results show that the proposed framework can not only speed up grasp planning with more stable configurations, but also is able to handle unknown objects. Furthermore, our framework can handle cluttered scenarios. A new Grasp Type Dataset (GTD) that considers 6 commonly used grasp types and covers 12 household objects is also presented.
