Tell me what this is: Few-shot incremental object learning by a robot

Ali Ayub, Alan R. Wagner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8344-8350
Number of pages7
ISBN (Electronic)9781728162126
DOIs
StatePublished - Oct 24 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
Duration: Oct 24 2020Jan 24 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
CountryUnited States
CityLas Vegas
Period10/24/201/24/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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