The CORTEX Cognitive Robotics Architecture: use cases

Pablo Bustos, Luis J. Manso, Antonio J. Bandera, Juan P. Bandera, Ismael Garcia-Varea, Jesus Martinez-Gomez

Research output: Contribution to journalArticle

Abstract

CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as "the person is talking and gazing at me". The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.
Original languageEnglish
Pages (from-to)107-123
Number of pages17
JournalCognitive Systems Research
Volume55
Early online date19 Jan 2019
DOIs
Publication statusPublished - 1 Jun 2019

Fingerprint

Robotics
Robots
Aptitude
Intelligence
Software
Human robot interaction
Actuators
Decomposition
Sensors

Bibliographical note

© 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Cognitive robotics
  • Robot control architectures

Cite this

Bustos, P., Manso, L. J., Bandera, A. J., Bandera, J. P., Garcia-Varea, I., & Martinez-Gomez, J. (2019). The CORTEX Cognitive Robotics Architecture: use cases. Cognitive Systems Research, 55, 107-123. https://doi.org/10.1016/j.cogsys.2019.01.003
Bustos, Pablo ; Manso, Luis J. ; Bandera, Antonio J. ; Bandera, Juan P. ; Garcia-Varea, Ismael ; Martinez-Gomez, Jesus. / The CORTEX Cognitive Robotics Architecture: use cases. In: Cognitive Systems Research. 2019 ; Vol. 55. pp. 107-123.
@article{fd7c6fb4bb724cb7b702c569db0af1b9,
title = "The CORTEX Cognitive Robotics Architecture: use cases",
abstract = "CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as {"}the person is talking and gazing at me{"}. The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.",
keywords = "Cognitive robotics, Robot control architectures",
author = "Pablo Bustos and Manso, {Luis J.} and Bandera, {Antonio J.} and Bandera, {Juan P.} and Ismael Garcia-Varea and Jesus Martinez-Gomez",
note = "{\circledC} 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",
year = "2019",
month = "6",
day = "1",
doi = "10.1016/j.cogsys.2019.01.003",
language = "English",
volume = "55",
pages = "107--123",

}

Bustos, P, Manso, LJ, Bandera, AJ, Bandera, JP, Garcia-Varea, I & Martinez-Gomez, J 2019, 'The CORTEX Cognitive Robotics Architecture: use cases', Cognitive Systems Research, vol. 55, pp. 107-123. https://doi.org/10.1016/j.cogsys.2019.01.003

The CORTEX Cognitive Robotics Architecture: use cases. / Bustos, Pablo; Manso, Luis J.; Bandera, Antonio J.; Bandera, Juan P.; Garcia-Varea, Ismael; Martinez-Gomez, Jesus.

In: Cognitive Systems Research, Vol. 55, 01.06.2019, p. 107-123.

Research output: Contribution to journalArticle

TY - JOUR

T1 - The CORTEX Cognitive Robotics Architecture: use cases

AU - Bustos, Pablo

AU - Manso, Luis J.

AU - Bandera, Antonio J.

AU - Bandera, Juan P.

AU - Garcia-Varea, Ismael

AU - Martinez-Gomez, Jesus

N1 - © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2019/6/1

Y1 - 2019/6/1

N2 - CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as "the person is talking and gazing at me". The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.

AB - CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as "the person is talking and gazing at me". The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.

KW - Cognitive robotics

KW - Robot control architectures

UR - https://www.sciencedirect.com/science/article/pii/S1389041717300347?dgcid=raven_sd_search_email

UR - http://www.scopus.com/inward/record.url?scp=85060622773&partnerID=8YFLogxK

U2 - 10.1016/j.cogsys.2019.01.003

DO - 10.1016/j.cogsys.2019.01.003

M3 - Article

VL - 55

SP - 107

EP - 123

ER -

Bustos P, Manso LJ, Bandera AJ, Bandera JP, Garcia-Varea I, Martinez-Gomez J. The CORTEX Cognitive Robotics Architecture: use cases. Cognitive Systems Research. 2019 Jun 1;55:107-123. https://doi.org/10.1016/j.cogsys.2019.01.003