Towards a Robot Architecture for Situated Lifelong Object Learning

Jose L. Part, Oliver Lemon

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

1 Citation (Scopus)

Abstract

The ability to acquire knowledge incrementally and after deployment is of utmost importance for robots operating in the real world. Moreover, robots that have to operate alongside people need to be able to interact in a way that is intuitive for the users, e.g., by understanding and producing natural language. In this paper we present a first prototype of a robot architecture developed for situated lifelong object learning. The system is able to communicate with its users through natural language and perform object learning and recognition on the spot through situated interactions. In this first stage, we evaluate the system in terms of recognition accuracy which gives an indirect measure of the quality of the collected data with the proposed pipeline. Our results show that the robot can use this data for both learning and recognition with acceptable incremental performance. We also discuss limitations and steps that are necessary in order to improve performance as well as to shed some light on system usability.

Original languageEnglish
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages1854-1860
Number of pages7
ISBN (Electronic)9781728140049
DOIs
Publication statusPublished - 27 Jan 2020
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems - Macau, China
Duration: 4 Nov 20198 Nov 2019
https://www.iros2019.org/

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2019
Country/TerritoryChina
CityMacau
Period4/11/198/11/19
Internet address

ASJC Scopus subject areas

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

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