A Toolkit to Generate Social Navigation Datasets


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.

Publication DOI: https://doi.org/10.1007/978-3-030-62579-5_13
Additional Information: © 2020 The Authors
Event Title: 21st International Workshop of Physical Agents (WAF2020)
Event Type: Other
Event Dates: 2020-11-19 - 2020-11-20
Uncontrolled Keywords: Navigation dataset,Robot simulation,Social navigation,Social robotics,Control and Systems Engineering,General Computer Science
ISBN: 978-3-030-62578-8, 978-3-030-62579-5
Last Modified: 16 Jul 2024 07:31
Date Deposited: 26 Nov 2020 13:47
Full Text Link: https://github. ... jmanso/sonata u
https://arxiv.o ... /abs/2009.05345
Related URLs: https://link.sp ... -030-62579-5_13 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2020-11-03
Accepted Date: 2020-09-02
Authors: Baghel, Rishabh
Kapoor, Aditya
Bachiller, Pilar
Jorvekar, Ronit R.
Rodriguez-Criado, Daniel
Manso, Luis J. (ORCID Profile 0000-0003-2616-1120)



Version: Draft Version

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