Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advances in Cognitive Robotics and Control
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Cognitive Robotics and Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2901

Special Issue Editors

Dr. Alessandro Umbrico

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of Cognitive Sciences and Technologies, 00185 Rome, Italy
Interests: automated planning and scheduling; timeline-based planning; robust execution; human–robot collaboration; knowledge representation and reasoning; cognitive architectures
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Beßler

E-Mail Website
Guest Editor
Institute for Artificial Intelligence, University of Bremen, Bremen, Germany
Interests: symbolic AI methods in the context of autonomous robots, and combinations of different methods in hybrid systems
Prof. Dr. Nele Russwinkel

E-Mail Website
Guest Editor
Institute of Information Systems, Universität zu Lübeck, Lubeck, Germany
Interests: AI; cognitive architectures; human-aware AI; cognitive science; sense of control

Special Issue Information

Dear Colleagues,

Robotics and artificial intelligence (AI) are two research areas that have historically addressed the challenge (among others) of building embedded intelligent systems capable of acting in a real-world environment. A synergetic interaction between these two disciplines is necessary in the development and deployment of robots that safely and reliably act in the real world. Furthermore, contributions from cognitive science are necessary in supporting “social qualities” and implementing socially compliant behaviors that are acceptable to humans. This is especially true when considering scenarios that entail the co-existence and/or continuous direct or indirect interactions with humans. The presence of humans indeed introduces a significant source of uncertainty which can affect robot control. The behavior of a human is unpredictable, including their intentions, desires, and objectives. Robot controllers cannot reliably predict human behaviors, and should therefore synthesize suitable strategies to safely perform actions. Robot controllers should take into account the “non-functional” qualities of implemented behaviors, thus evolving towards an advanced “Perception, Reason, Act” paradigm which can achieve a higher level of awareness and behavior contextualization. Cognitive robotics is wide research area, fostering the interaction of robotics, AI,  and cognitive sciences in order to realize innovative and human-like robot behaviors. In this regard, this Special Issue aims to collect contributions in this multidisciplinary research landscape, highlighting recent trends, novel results, and open issues in the design of human and socially aware robot controllers.

Dr. Alessandro Umbrico
Dr. Daniel Beßler
Prof. Dr. Nele Russwinkel
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 submissions that pass pre-check are 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 2400 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.

Keywords

  • cognitive architectures
  • human-robot interaction
  • perspective taking
  • cognitive robotics
  • artificial intelligence
  • situation awareness
  • human and social awareness
  • human intent/action recognition
  • human belief modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 1255 KiB  
Article
Retrieving Memory Content from a Cognitive Architecture by Impressions from Language Models for Use in a Social Robot
by Thomas Sievers and Nele Russwinkel
Appl. Sci. 2025, 15(10), 5778; https://doi.org/10.3390/app15105778 - 21 May 2025
Abstract
Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of [...] Read more.
Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of memory. We investigate a way to create a social robot with a human-like memory and recollection based on cognitive processes for a better comprehensible and situational behavior of the robot. Using a combined system consisting of an Adaptive Control of Thought-Rational (ACT-R) model and a humanoid social robot, we show how recollections from the declarative memory of the ACT-R model can be retrieved using data obtained by the robot via an LLM or VLM, processed according to the procedural memory of the cognitive model and returned to the robot as instructions for action. Real-world data captured by the robot can be stored as memory chunks in the cognitive model and recalled, for example by means of associations. This opens up possibilities for using human-like judgment and decision-making capabilities inherent in cognitive architectures with social robots and practically offers opportunities of augmenting the prompt for LLM-driven utterances with content from declarative memory, thus keeping them more contextually relevant. We illustrate the use of such an approach in HRI scenarios with the social robot Pepper. Full article
(This article belongs to the Special Issue Advances in Cognitive Robotics and Control)
16 pages, 3403 KiB  
Article
Beyond Binary Dialogues: Research and Development of a Linguistically Nuanced Conversation Design for Social Robots in Group–Robot Interactions
by Christoph Bensch, Ana Müller, Oliver Chojnowski and Anja Richert
Appl. Sci. 2024, 14(22), 10316; https://doi.org/10.3390/app142210316 - 9 Nov 2024
Cited by 2 | Viewed by 1234
Abstract
In this paper, we detail the technical development of a conversation design that is sensitive to group dynamics and adaptable, taking into account the subtleties of linguistic variations between dyadic (i.e., one human and one agent) and group interactions in human–robot interaction (HRI) [...] Read more.
In this paper, we detail the technical development of a conversation design that is sensitive to group dynamics and adaptable, taking into account the subtleties of linguistic variations between dyadic (i.e., one human and one agent) and group interactions in human–robot interaction (HRI) using the German language as a case study. The paper details the implementation of robust person and group detection with YOLOv5m and the expansion of knowledge databases using large language models (LLMs) to create adaptive multi-party interactions (MPIs) (i.e., group–robot interactions (GRIs)). We describe the use of LLMs to generate training data for socially interactive agents including social robots, as well as a self-developed synthesis tool, knowledge expander, to accurately map the diverse needs of different users in public spaces. We also outline the integration of a LLM as a fallback for open-ended questions not covered by our knowledge database, ensuring it can effectively respond to both individuals and groups within the MPI framework. Full article
(This article belongs to the Special Issue Advances in Cognitive Robotics and Control)
►▼ Show Figures

Figure 1

17 pages, 16821 KiB  
Article
Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots
by Noé Zapata, Gerardo Pérez, Lucas Bonilla, Pedro Núñez, Pilar Bachiller and Pablo Bustos
Appl. Sci. 2024, 14(17), 8057; https://doi.org/10.3390/app14178057 - 9 Sep 2024
Viewed by 996
Abstract
The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such [...] Read more.
The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a “like-me” policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm’s performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system’s adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm’s effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm’s performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems. Full article
(This article belongs to the Special Issue Advances in Cognitive Robotics and Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop