Human-robot dialogue and Collaboration for social navigation in crowded environments

Carlos Lobato, Araceli Vega-Magro, Pedro Núñez, Luis J. Manso

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

Abstract

Robot navigation in human-populated environments is a subject of great interest among the international scientific community. In order to be accepted in these scenarios, it is important for robots to navigate respecting social rules. Avoid getting too close to a person, not interrupting conversations or asking for permission or collaboration when it is required by social conventions, are some of the behaviours that robots must exhibit. This paper presents a social navigation system that integrates different software agents within a cognitive architecture for robots and describes, as the main contribution, the corpus that allows to establish dialogues between robots and humans in real situations to improve the human-aware navigation system. The corpus has been experimentally evaluated by the simulation of different daily situations, where robots need to plan interactions with real people. The results are analysed qualitatively, according to the behaviour expected by the robot in the interaction performed. The results show how the corpus presented in this paper improves the robot navigation, making it more socially accepted.

Original languageEnglish
Title of host publication19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019
EditorsLuis Almeida, Luis Paulo Reis, Antonio Paulo Moreira
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-3558-8
ISBN (Print)978-1-7281-3559-5
DOIs
Publication statusPublished - 10 Jun 2019

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  • Cite this

    Lobato, C., Vega-Magro, A., Núñez, P., & Manso, L. J. (2019). Human-robot dialogue and Collaboration for social navigation in crowded environments. In L. Almeida, L. P. Reis, & A. P. Moreira (Eds.), 19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019 [8733641] IEEE. https://doi.org/10.1109/ICARSC.2019.8733641