TAILOR: Teaching with Active and Incremental Learning for Object Registration

• URL: http://arxiv.org/abs/2205.11692v1
• Date: Tue, 24 May 2022 01:14:00 GMT
• Title: TAILOR: Teaching with Active and Incremental Learning for Object Registration
• Authors: Qianli Xu, Nicolas Gauthier, Wenyu Liang, Fen Fang, Hui Li Tan, Ying Sun, Yan Wu, Liyuan Li, Joo-Hwee Lim
• Abstract summary: We present TAILOR -- a method and system for object registration with active and incremental learning. 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.
• Score: 18.941458386996544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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