TAILOR: 能動的・漸進的学習による物体登録の教示
TAILOR: Teaching with Active and Incremental Learning for Object Registration
ロボットが新しい物体を効率的に登録できるよう、能動的な視点選択と忘却を防ぐ漸進的学習を組み合わせたシステムTAILORを提案し、実世界のギアボックス組立作業で有効性を示した。
著者: Qianli Xu, Nicolas Gauthier, Wenyu Liang, Fen Fang, Hui Li Tan, Ying Sun, Yan Wu, Liyuan Li, Joo-Hwee Lim
分類: cs.RO, cs.AI
原文アブストラクト
When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and labor-intensive. We present TAILOR -- a method and system for object registration with active and incremental learning. When instructed by a human teacher to register an object, TAILOR is able to automatically select viewpoints to capture informative images by actively exploring viewpoints, and employs a fast incremental learning algorithm to learn new objects without potential forgetting of previously learned objects. We demonstrate the effectiveness of our method with a KUKA robot to learn novel objects used in a real-world gearbox assembly task through natural interactions.
