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.
@article{arxiv.2205.11692,
title = {TAILOR: Teaching with Active and Incremental Learning for Object Registration},
author = {Qianli Xu and Nicolas Gauthier and Wenyu Liang and Fen Fang and Hui Li Tan and Ying Sun and Yan Wu and Liyuan Li and Joo-Hwee Lim},
journal= {arXiv preprint arXiv:2205.11692},
year = {2022}
}