2026-05-27T16:25:33Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/97602026-02-03T12:05:24Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Romero-Garces, Adrian
author
Calderita, Luis Vicente
author
Martínez-Gómez, Jesús
author
Bandera-Rubio, Juan Pedro
author
Marfil-Robles, Rebeca
author
Manso, Luis J.
author
Bandera-Rubio, Antonio Jesús
author
Bustos, Pablo
2015-05-12T12:11:24Z
2015-05-12T12:11:24Z
2015-05-12
http://hdl.handle.net/10630/9760
Over the past decades, the number of robots deployed in museums, trade shows and exhibitions have grown steadily. This new application domain has become a key research topic in the robotics community. Therefore, new robots are designed to interact with people in these domains, using natural and intuitive channels. Visual perception and speech processing have
to be considered for these robots, as they should be able to detect people in their environment, recognize their degree of accessibility and engage them in social conversations. They also need to safely navigate around dynamic, uncontrolled environments. They must be equipped with planning and learning components, that allow them to adapt to different scenarios. Finally, they must attract the attention of the people, be kind and safe to interact with. In this paper, we describe our experience with Gualzru, a salesman robot endowed with the cognitive architecture RoboCog. This architecture synchronizes all previous processes in a social
robot, using a common inner representation as the core of the
system. The robot has been tested in crowded, public daily life
environments, where it interacted with people that had never seen
it before nor had a clue about its functionality. Experimental
results presented in this paper demonstrate the capabilities of
the robot and its limitations in these real scenarios, and define
future improvement actions.
eng
Robots autónomos
Testing a fully autonomous robotic salesman in real scenarios
conference output
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URL
https://riuma.uma.es/bitstreams/cceb4594-7df8-4c40-b906-358ea0e3c746/download
File
MD5
843ba653b64f088cdf5a09473c6bc936
20018350
application/pdf
Presentation_ICARSC_2015.pdf