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Online learning of human navigational intentions

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

Abstract

We present a novel approach for online learning of human intentions in the context of navigation and show its advantage in human tracking. The proposed approach assumes humans to be motivated to navigate with a set of imaginary social forces and continuously learns the preferences of each human to follow these forces. We conduct experiments both in simulation and real-world environments to demonstrate the feasibility of the approach and the benefit of employing it to track humans. The results show the correlation between the learned intentions and the actions taken by a human subject in controlled environments in the context of human-robot interaction.

Original languageEnglish (US)
Title of host publicationSocial Robotics - 10th International Conference, ICSR 2018, Proceedings
EditorsElizabeth Broadbent, Shuzhi Sam Ge, Miguel A. Salichs, Álvaro Castro-González, Hongsheng He, John-John Cabibihan, Alan R. Wagner
PublisherSpringer Verlag
Pages1-10
Number of pages10
ISBN (Print)9783030052034
DOIs
StatePublished - 2018
Externally publishedYes
Event10th International Conference on Social Robotics, ICSR 2018 - Qingdao, China
Duration: Nov 28 2018Nov 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11357 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Social Robotics, ICSR 2018
Country/TerritoryChina
CityQingdao
Period11/28/1811/30/18

Keywords

  • Human tracking
  • Human-robot interaction
  • Navigational intentions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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