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 f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/5/1/7 |
| _version_ | 1798026059676909568 |
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| author | Luis J. Manso Pedro Nuñez Luis V. Calderita Diego R. Faria Pilar Bachiller |
| author_facet | Luis J. Manso Pedro Nuñez Luis V. Calderita Diego R. Faria Pilar Bachiller |
| author_sort | Luis J. Manso |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:29:06Z |
| format | Article |
| id | doaj.art-c1be0e59923646fbab254f6226bdf4a4 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-11T18:29:06Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-c1be0e59923646fbab254f6226bdf4a42022-12-22T04:09:31ZengMDPI AGData2306-57292020-01-0151710.3390/data5010007data5010007SocNav1: A Dataset to Benchmark and Learn Social Navigation ConventionsLuis J. Manso0Pedro Nuñez1Luis V. Calderita2Diego R. Faria3Pilar Bachiller4Computer Science Department, School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UKRobotics and Artificial Vision Laboratory—RoboLab, Caceres School of Technology, Universidad de Extremadura, C10003 Caceres, Extremadura, SpainRobotics and Artificial Vision Laboratory—RoboLab, Caceres School of Technology, Universidad de Extremadura, C10003 Caceres, Extremadura, SpainComputer Science Department, School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UKRobotics and Artificial Vision Laboratory—RoboLab, Caceres School of Technology, Universidad de Extremadura, C10003 Caceres, Extremadura, SpainDatasets 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.https://www.mdpi.com/2306-5729/5/1/7social navigationhuman-aware navigationhuman–robot interactionnavigation datasetgraph dataset |
| spellingShingle | Luis J. Manso Pedro Nuñez Luis V. Calderita Diego R. Faria Pilar Bachiller SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions Data social navigation human-aware navigation human–robot interaction navigation dataset graph dataset |
| title | SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions |
| title_full | SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions |
| title_fullStr | SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions |
| title_full_unstemmed | SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions |
| title_short | SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions |
| title_sort | socnav1 a dataset to benchmark and learn social navigation conventions |
| topic | social navigation human-aware navigation human–robot interaction navigation dataset graph dataset |
| url | https://www.mdpi.com/2306-5729/5/1/7 |
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