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  4. Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd
 
conference paper

Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd

Grzeskowiak, Fabien
•
Gonon, David Julian  
•
Dugas, Daniel
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May 30, 2021
2021 IEEE International Conference on Robotics and Automation (ICRA)
2021 IEEE International Conference on Robotics and Automation (ICRA)

The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a benchmark tool to evaluate such capabilities; our long term vision is to provide the community with a simulation tool that generates virtual crowded environment to test robots, to establish standard scenarios and metrics to evaluate navigation techniques in terms of safety and efficiency, and thus, to install new methods to benchmarking robots’ crowd navigation capa- bilities. This paper presents the architecture of the simulation tools, introduces first scenarios and evaluation metrics, as well as early results to demonstrate that our solution is relevant to be used as a benchmark tool.

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Type
conference paper
DOI
10.1109/ICRA48506.2021.9561694
ArXiv ID

2104.14177

Author(s)
Grzeskowiak, Fabien
Gonon, David Julian  
Dugas, Daniel
Paez Granados, Diego Felipe  
Chung, Jen Jen
Nieto, Juan
Siegwart, Roland
Billard, Aude  orcid-logo
Babel, Marie
Pettré, Julien
Date Issued

2021-05-30

Publisher

IEEE

Published in
2021 IEEE International Conference on Robotics and Automation (ICRA)
ISBN of the book

978-1-728190-78-5

Total of pages

7

Note

CorwdBot Project.

URL

Code and Simulator

http://crowdbot.eu/crowdbot-challenge/
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LASA  
Event nameEvent placeEvent date
2021 IEEE International Conference on Robotics and Automation (ICRA)

Xi'an, China

30 May-5 June 2021

Available on Infoscience
October 22, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/182388
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