SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions


Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Different factors make designing and gathering data for human-aware navigation datasets challenging. Firstly, the problem itself is subjective, different dataset contributors will very frequently disagree to some extent on their labels. Secondly, the number of variables to consider is undetermined culture-dependent. This paper presents SocNav1, a dataset for social navigation conventions. SocNav1 aims at evaluating the robots’ ability to assess the level of discomfort that their presence might generate among humans. The 9280 samples in SocNav1 seem to be enough for machine learning purposes given the relatively small size of the data structures describing the scenarios. Furthermore, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as graph neural networks. This paper describes the proposed dataset and the method employed to gather the data. To provide a further understanding of the nature of the dataset, an analysis and validation of the collected data are also presented.

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Additional Information: ©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Uncontrolled Keywords: Graph dataset,Human,Human-aware navigation,Navigation dataset,Robot interaction,Social navigation,Computer Science Applications,Information Systems,Information Systems and Management
Publication ISSN: 2306-5729
Last Modified: 10 May 2024 07:14
Date Deposited: 14 Jan 2020 11:56
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Related URLs: https://www.mdp ... 2306-5729/5/1/7 (Publisher URL)
https://github. ... ljmanso/SocNav1 (Related URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-01-14
Accepted Date: 2020-01-09
Authors: Manso, Luis J. (ORCID Profile 0000-0003-2616-1120)
Nunez, Pedro
Calderita, Luis V.
Faria, Diego (ORCID Profile 0000-0002-2771-1713)
Bachiller, Pilar



Version: Published Version

License: Creative Commons Attribution

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