A Toolkit to Generate Social Navigation Datasets

Rishabh Baghel, Aditya Kapoor, Pilar Bachiller, Ronit R. Jorvekar, Daniel Rodriguez-Criado, Luis J. Manso

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

Abstract

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used.

Original languageEnglish
Title of host publicationAdvances in Physical Agents II - Proceedings of the 21st International Workshop of Physical Agents WAF 2020
EditorsLuis M. Bergasa, Manuel Ocaña, Rafael Barea, Elena López-Guillén, Pedro Revenga
PublisherSpringer
Pages180-193
Number of pages14
ISBN (Electronic)978-3-030-62579-5
ISBN (Print)978-3-030-62578-8
DOIs
Publication statusPublished - 3 Nov 2020
Event21st International Workshop of Physical Agents (WAF2020) - Madrid, Spain
Duration: 19 Nov 202020 Nov 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1285
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference21st International Workshop of Physical Agents (WAF2020)
CountrySpain
CityMadrid
Period19/11/2020/11/20

Bibliographical note

© 2020 The Authors

Keywords

  • Navigation dataset
  • Robot simulation
  • Social navigation
  • Social robotics

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