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Electronics
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Human Robot Interaction: Techniques, Applications, and Future Trends
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Human Robot Interaction: Techniques, Applications, and Future Trends

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2010

Special Issue Editors

Dr. Iustin Priescu

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Guest Editor
Faculty of Informatics, Titu Maiorescu University, 040051 Bucharest, Romania
Interests: innovation; cybersecurity; cloud computing; data science; artificial intelligence; robotics; business continuity
Prof. Dr. Ionica Oncioiu

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Guest Editor
Faculty of Informatics, Titu Maiorescu University, 040051 Bucharest, Romania
Interests: data science; autonomous robot; innovation and digital transformation; artificial intelligence; integrated solution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human Robot Interaction (HRI) is an expanding field at the intersection of robotics, artificial intelligence, psychology, interactive design, and cognitive sciences. As technologies advance and robots become increasingly present in everyday environments—from industry and education to healthcare and smart homes—the efficient, safe, and intuitive interaction between humans and robots is gaining ever greater importance. This interaction not only targets technical aspects but also trust, social acceptance, natural communication, and the adaptability of robots to human needs and behaviors. In this context, research in HRI offers vast opportunities for the development of innovative solutions that can transform the way people collaborate with robotic systems in various areas of life.

This Special Issue in the Electronics journal aims to bring together the latest research and applications in the field of HRI, focusing on emerging techniques, concrete implementations, and future development directions. Contributions that address the technical, social, and ethical aspects of human robot interaction and propose creative solutions, demonstrative platforms, or relevant case studies are encouraged.

The topics of interest include, but are not limited to, the following:

  • Machine learning and artificial intelligence algorithms for human–robot collaboration;
  • Multimodal interaction methods (speech, gestures, gaze, and haptic feedback);
  • Recognition of emotions and adaptive behaviors in robots;
  • Human factors and ergonomics in HRI interface design;
  • Social and assistive robotics for health, therapy, and elderly care;
  • Educational robots and platforms for collaborative learning;
  • Trust, ethics, and transparency in interaction with robots;
  • Augmented and virtual reality in HRI systems;
  • The safety and reliability of robots near humans;
  • Future trends and emerging applications of HRI in smart environments;
  • Natural and personalized interaction with robots;
  • Perception technologies and advanced sensors for context understanding;
  • Autonomous systems with social capabilities;
  • Co-evolution of human–robot behavior in hybrid teams;
  • Designing robots for inclusion and accessibility;
  • Testing and validation of HRI interfaces in real or simulated environments;
  • Standardization and interoperability in HRI systems;
  • HRI applications in agriculture, logistics, defense, or emergency response.

Dr. Iustin Priescu
Prof. Dr. Ionica Oncioiu
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. Electronics 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

  • human robot interaction (HRI)
  • socially assistive robot
  • multimodal interaction
  • emotion recognition
  • collaborative robotics
  • intelligent systems
  • machine learning
  • trust in robots
  • human-centered design
  • ethics in robotics

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Published Papers (3 papers)

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Research

14 pages, 4969 KB  
Article
Learning Human–Robot Proxemics Models from Experimental Data
by Qiaoyue Yang, Lukas Kachel, Magnus Jung, Ayoub Al-Hamadi and Sven Wachsmuth
Electronics 2025, 14(18), 3704; https://doi.org/10.3390/electronics14183704 - 18 Sep 2025
Viewed by 480
Abstract
Humans in a society generally tend to implicitly adhere to the shared social norms established within that culture. Robots operating in a dynamic environment shared with humans are also expected to behave socially to improve their interaction and enhance their likability among humans. [...] Read more.
Humans in a society generally tend to implicitly adhere to the shared social norms established within that culture. Robots operating in a dynamic environment shared with humans are also expected to behave socially to improve their interaction and enhance their likability among humans. Especially when moving into close proximity of their human partners, robots should convey perceived safety and intelligence. In this work, we model human proxemics as robot navigation costs, allowing the robot to exhibit avoidance behavior around humans or to initiate interactions when engagement is required. The proxemic model enhances robot navigation by incorporating human-aware behaviors, treating humans not as mere obstacles but as social agents with personal space preferences. The model of interaction positions estimates suitable locations relative to the target person for the robot to approach when an engagement occurs. Our evaluation on human–robot interaction data and simulation experiments demonstrates the effectiveness of the proposed models in guiding the robot’s avoidance and approaching behaviors toward humans. Full article
(This article belongs to the Special Issue Human Robot Interaction: Techniques, Applications, and Future Trends)
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18 pages, 2575 KB  
Article
Gestures in Motion: Exploring Referent-Free Elicitation Method for Hexapod Robot Control in Urban Environments
by Natalia Walczak, Julia Trzebuchowska, Wiktoria Krzyżańska, Franciszek Sobiech, Aleksandra Wysokińska, Andrzej Romanowski and Krzysztof Grudzień
Electronics 2025, 14(18), 3667; https://doi.org/10.3390/electronics14183667 - 16 Sep 2025
Viewed by 459
Abstract
Gesture elicitation studies (GES) is a promising method for intuitive interaction with mobile robots in urban environments. Traditional gesture elicitation methods rely on predefined commands, which may restrict creativity and adaptability. This study explores referent-free gesture elicitation as a method for discovering natural, [...] Read more.
Gesture elicitation studies (GES) is a promising method for intuitive interaction with mobile robots in urban environments. Traditional gesture elicitation methods rely on predefined commands, which may restrict creativity and adaptability. This study explores referent-free gesture elicitation as a method for discovering natural, user-defined gestures to control a hexapod robot. Through a three-phase user study, we explore gesture diversity, user confidence, and agreement rates across tasks. Results show that referent-free methods foster creativity but present consistency challenges, while referent-based approaches offer better convergence for familiar commands. As a proof of concept, we implemented a subset of gestures on an embedded platform using a stereo vision system and tested live classification with two gestures. This technical extension demonstrates early-stage feasibility and informs future deployments. Our findings contribute a design framework for human-centered gesture interfaces in mobile robotics, especially for dynamic public environments. Full article
(This article belongs to the Special Issue Human Robot Interaction: Techniques, Applications, and Future Trends)
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23 pages, 3155 KB  
Article
Construction of a Machining Process Knowledge Graph and Its Application in Process Route Recommendation
by Liang Li, Jiaxing Liang, Chunlei Li, Zhe Liu, Yingying Wei and Zeyu Ji
Electronics 2025, 14(15), 3156; https://doi.org/10.3390/electronics14153156 - 7 Aug 2025
Viewed by 656
Abstract
This paper proposes a knowledge graph (KG) construction method for a part machining process in response to the low degree of structuring of historical process data association relationships within the enterprise in the field of part machining, which makes it difficult to reuse [...] Read more.
This paper proposes a knowledge graph (KG) construction method for a part machining process in response to the low degree of structuring of historical process data association relationships within the enterprise in the field of part machining, which makes it difficult to reuse effectively. The part types are mainly shafts, gears, boxes and other common parts. First, the schema layer of the process knowledge graph was constructed using a top-down approach. Second, deep learning techniques were employed for entity extraction, while knowledge fusion and ontology relationship establishment methods were combined to build the data layer of the process knowledge graph (PKG) from the bottom up. Third, the mapping between the schema layer and data layer was implemented in the Neo4j graph database. Based on the constructed process KG, process route recommendation and rapid retrieval of process information were thus accomplished. Finally, a shaft part was used as the target part to verify the effectiveness of the proposed method. In over 300 trials, the similarity-based recommendation model achieved a hit rate of 91.7% (the target part’s route appeared in the recommended list in 91.7% of cases). These results indicate that the proposed machining PKG construction is feasible and can assist in process planning, potentially improving the efficiency of retrieving and reusing machining knowledge. Full article
(This article belongs to the Special Issue Human Robot Interaction: Techniques, Applications, and Future Trends)
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