@inproceedings{f5696ef7e3c74bd6b105e9de23d7f5b8,
title = "Tell me what this is: Few-shot incremental object learning by a robot",
abstract = "For many applications, robots will need to be incrementally trained to recognize the specific objects needed for an application. This paper presents a practical system for incrementally training a robot to recognize different object categories using only a small set of visual examples provided by a human. The paper uses a recently developed state-of-the-art method for few-shot incremental learning of objects. After learning the object classes incrementally, the robot performs a table cleaning task organizing objects into categories specified by the human. We also demonstrate the system's ability to learn arrangements of objects and predict missing or incorrectly placed objects. Experimental evaluations demonstrate that our approach achieves nearly the same performance as a system trained with all examples at one time (batch training), which constitutes a theoretical upper bound.",
author = "Ali Ayub and Wagner, {Alan R.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 ; Conference date: 24-10-2020 Through 24-01-2021",
year = "2020",
month = oct,
day = "24",
doi = "10.1109/IROS45743.2020.9341140",
language = "English (US)",
series = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8344--8350",
booktitle = "2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020",
address = "United States",
}