ドメイン非依存の教師なし学習による新規物体の把持
Domain Independent Unsupervised Learning to grasp the Novel Objects
画像平面上でk-meansクラスタリングを行い把持領域を抽出し、Grasp Decide Indexで最良把持姿勢を選ぶ教師なし学習ベースの把持手法を提案した。
著者: Siddhartha Vibhu Pharswan, Mohit Vohra, Ashish Kumar, Laxmidhar Behera
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
One of the main challenges in the vision-based grasping is the selection of feasible grasp regions while interacting with novel objects. Recent approaches exploit the power of the convolutional neural network (CNN) to achieve accurate grasping at the cost of high computational power and time. In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions. Unsupervised learning infers the pattern in data-set without any external labels. We apply k-means clustering on the image plane to identify the grasp regions, followed by an axis assignment method. We define a novel concept of Grasp Decide Index (GDI) to select the best grasp pose in image plane. We have conducted several experiments in clutter or isolated environment on standard objects of Amazon Robotics Challenge 2017 and Amazon Picking Challenge 2016. We compare the results with prior learning based approaches to validate the robustness and adaptive nature of our algorithm for a variety of novel objects in different domains.
