Special Issue "Cognitive Robotics"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 15 December 2020.

Special Issue Editors

Prof. Antonio Bandera
Website
Guest Editor
University of Malaga, Spain
Interests: assistive robotics; embedded vision
Dr. Luis J. Manso
Website
Guest Editor
Aston University, UK
Interests: active perception; cognitive architectures; service robots
Dr. Zoe Falomir
Website
Guest Editor
Bremen Spatial Cognition Center, University of Bremen, Germany
Interests: spatial reasoning; cognitive systems

Special Issue Information

Dear Colleagues,

There is a growing desire to develop robots that are capable of helping humans with daily tasks. Cognitive robots need to explore and understand their environment, choose a safe and human-aware course of action, and learn—not only from experience but also through interaction. In particular, cognitive robotics aims to endow robots with the capacity to plan solutions for complex goals and to enact those plans while being reactive to unexpected changes in their environments. Among the limiting factors for their application in real-life scenarios, there are clearly ethical, technological, and economic challenges.

Cognitive robotics includes studies on advanced mechatronics, artificial intelligence, and machine learning, as well as cognitive psychology and brain science in the frame of cognitive science. The aim of this Special Issue is to gather scientific papers addressing any of the challenges of cognitive robotics. The topics of this Special Issue include, but are not limited to, the following:

  • Active perception;
  • Architectures and frameworks for cognition;
  • Cognitive human–robot interaction;
  • Cognitive modeling and development;
  • Knowledge discovery and representation in robots;
  • Learning for action and interaction;
  • Cognitive architectures for interactive robots;
  • Neurorobotics;
  • Social and assistive robots.

Prof. Antonio Bandera
Dr. Luis J. Manso
Dr. Zoe Falomir
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Open AccessArticle
A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment
Appl. Sci. 2020, 10(15), 5147; https://doi.org/10.3390/app10155147 - 27 Jul 2020
Abstract
Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To [...] Read more.
Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To overcome this problem, in this work, an agent is computationally devised that mimics the grid and place neuron functionality to learn cognitive maps from the input spatial data of an environment or an object. A novel quadrant-based approach is proposed to model the behavior of the grid neuron, which, like the real grid neuron, is capable of generating periodic hexagonal grid-like output patterns from the input body movement. Furthermore, a cognitive map formation and their learning mechanism are proposed using the place–grid neuron interaction system, which is meant for making predictions of environmental sensations from the body movement. A place sequence learning system is also introduced, which is like an episodic memory of a trip that is forgettable based on their usage frequency and helps in reducing the accumulation of error during a visit to distant places. The model has been deployed and validated in two different spatial data learning applications, one being the 2D object detection by touch, and another is the navigation in an environment. The result analysis shows that the proposed model is significantly associated with the expected outcomes. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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Open AccessArticle
On Cognitive Assistant Robots for Reducing Variability in Industrial Human-Robot Activities
Appl. Sci. 2020, 10(15), 5137; https://doi.org/10.3390/app10155137 - 26 Jul 2020
Abstract
In the industrial domain, one important research activity for cognitive robotics is the development of assistant robots. In this work, we show how the use of a cognitive assistant robot can contribute to (i) improving task effectiveness and productivity, (ii) providing autonomy for [...] Read more.
In the industrial domain, one important research activity for cognitive robotics is the development of assistant robots. In this work, we show how the use of a cognitive assistant robot can contribute to (i) improving task effectiveness and productivity, (ii) providing autonomy for the human supervisor to make decisions, providing or improving human operators’ skills, and (iii) giving feedback to the human operator in the loop. Our approach is evaluated on variability reduction in a manual assembly system. The overall study and analysis are performed on a model of the assembly system obtained using the Functional Resonance Analysis Method (FRAM) and tested in a robotic simulated scenario. Results show that a cognitive assistant robot is a useful partner in the role of improving the task effectiveness of human operators and supervisors. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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