Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Intelligent Social Robotic Systems
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Social Robotic Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 1 June 2025 | Viewed by 11776

Special Issue Editors

Dr. Ameersing Luximon

E-Mail Website
Guest Editor
EMEDS Limited, Kowloon, Hong Kong
Interests: social robots; ergonomics in design; wearable products; design
Prof. Dr. Ravindra S. Goonetilleke

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
Interests: human-centered product and service design; human–computer interaction; psychophysical modelling; data visualization

Special Issue Information

Dear Colleagues,

Intelligent robots are widely used in industries; however, with development of ubiquitous mobile technologies, virtual presence and communication networks, and enhanced AI systems, there has been a shift in the trend and development of intelligent robots, one of them being the development of social robots. Social robots are not only found in the realms of science fiction, but also reality. Although we tend to envision social robots to be similar to us, with a head and human-like features, with the development of technology, information systems, AI systems, smart cities, and smart homes, social robots can take different forms and shapes. In addition, the way we interact with robots in general is evolving, as there is progressively more acceptance of robots in people lives. Social robots enable interactions between humans, other robots, and smart systems. Recently, due to COVID-19, there has been an increased use of social robots to greet and provide customer services. Furthermore, with the aging population, it is anticipated that social robots will be widely used in health care and service sectors for active aging. 

The development of intelligent and smart social robots is faced with many challenges in terms of the design; interaction with humans; data collection; instrumentation and sensing technologies; AI technologies; and communication with other smart systems and social robots. Social robot challenges are evolving as the users adapt to the availability of smart systems. This Special Issue focuses mainly on intelligent robots with an emphasis on social robots; however, it is not limited to purely that topic. Any research in terms of instrumentation, sensing, visualization, design, interaction, data analysis, modelling, and applications of robots in business, public, or private settings will be considered. Well written research papers on design concepts, the future of social robots, surveys, and the literature will also be considered. 

Dr. Ameersing Luximon
Prof. Dr. Ravindra S. Goonetilleke
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. Sensors 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 2600 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

  • social robots: past, present, and future
  • theoretical aspects of robotic systems, including sensing and instrumentation
  • robotics systems and technology
  • wearables technology and systems for social robots
  • AI technologies in intelligent robotic systems
  • human–robot interaction
  • design of intelligent social robotic systems
  • intelligent robotic systems in everyday life
  • instrumentation and sensors for intelligent robotic systems
  • smart homes with social systems
  • smart cities and social robots
  • social robots in health care

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 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

12 pages, 1559 KiB  
Article
Placing Objects on Table Is Preferred over Direct Handovers When Users Are Occupied
by Thieu Long Phan and Akansel Cosgun
Sensors 2025, 25(7), 2140; https://doi.org/10.3390/s25072140 - 28 Mar 2025
Viewed by 198
Abstract
Service robots commonly deliver objects through direct handovers, assuming users are fully attentive. However, in real-world scenarios, users are often occupied with other tasks. This paper investigates how user attentiveness affects preferences between direct handovers and placing objects on a table. A user [...] Read more.
Service robots commonly deliver objects through direct handovers, assuming users are fully attentive. However, in real-world scenarios, users are often occupied with other tasks. This paper investigates how user attentiveness affects preferences between direct handovers and placing objects on a table. A user study was conducted (n = 25) to evaluate these strategies in scenarios where participants were either occupied (simulated via a typing task) or unoccupied. Results show that placing objects on the table significantly enhances user experience when users were occupied, with higher ratings for satisfaction, perceived safety, confidence in robot ability and intuitiveness of interaction. While direct handovers performed better with unoccupied users compared to occupied users, table placement maintained consistently high performance regardless of user state. All participants preferred table placement when occupied, and the majority preferred it even when unoccupied. These findings suggest table placement should be the default object delivery strategy for service robots, particularly in environments where user attention may vary. We also discuss implications for robot design and propose future directions for adaptive delivery behaviors. Full article
(This article belongs to the Special Issue Intelligent Social Robotic Systems)
►▼ Show Figures

Figure 1

14 pages, 6903 KiB  
Communication
Development of Dual-Arm Human Companion Robots That Can Dance
by Joonyoung Kim, Taewoong Kang, Dongwoon Song, Gijae Ahn and Seung-Joon Yi
Sensors 2024, 24(20), 6704; https://doi.org/10.3390/s24206704 - 18 Oct 2024
Viewed by 1161
Abstract
As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human–robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures [...] Read more.
As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human–robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject. Full article
(This article belongs to the Special Issue Intelligent Social Robotic Systems)
►▼ Show Figures

Figure 1

16 pages, 11069 KiB  
Article
Human-to-Robot Handover Based on Reinforcement Learning
by Myunghyun Kim, Sungwoo Yang, Beomjoon Kim, Jinyeob Kim and Donghan Kim
Sensors 2024, 24(19), 6275; https://doi.org/10.3390/s24196275 - 27 Sep 2024
Viewed by 1420
Abstract
This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots’ ability to safely exchange diverse objects with humans during human–robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and [...] Read more.
This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots’ ability to safely exchange diverse objects with humans during human–robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand’s adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs. Full article
(This article belongs to the Special Issue Intelligent Social Robotic Systems)
►▼ Show Figures

Figure 1

22 pages, 4641 KiB  
Article
Social Type-Aware Navigation Framework for Mobile Robots in Human-Shared Environments
by Sumin Kang, Sungwoo Yang, Daewon Kwak, Yura Jargalbaatar and Donghan Kim
Sensors 2024, 24(15), 4862; https://doi.org/10.3390/s24154862 - 26 Jul 2024
Cited by 1 | Viewed by 1457
Abstract
As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics [...] Read more.
As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics or their age. In real-world social spaces, there are many different types of people, and robots need to be more sensitive, especially when interacting with vulnerable subjects such as children. However, the current navigation methods do not consider these differences and apply the same avoidance strategies to everyone. Thus, we propose a new navigation framework that considers different social types and defines appropriate personal spaces for each, allowing robots to respect them. To this end, the robot needs to classify people in a real environment into social types and define the personal space for each type as a Gaussian asymmetric function to respect them. The proposed framework is validated through simulations and real-world experiments, demonstrating that the robot can improve the quality of interactions with people by providing each individual with an adaptive personal space. The proposed costmap layer is available on GitHub. Full article
(This article belongs to the Special Issue Intelligent Social Robotic Systems)
►▼ Show Figures

Figure 1

22 pages, 2326 KiB  
Article
NEP+: A Human-Centered Framework for Inclusive Human-Machine Interaction Development
by Enrique Coronado, Natsuki Yamanobe and Gentiane Venture
Sensors 2023, 23(22), 9136; https://doi.org/10.3390/s23229136 - 12 Nov 2023
Cited by 2 | Viewed by 2700
Abstract
This article presents the Network Empower and Prototyping Platform (NEP+), a flexible framework purposefully crafted to simplify the process of interactive application development, catering to both technical and non-technical users. The name "NEP+" encapsulates the platform’s dual mission: to empower the network-related capabilities [...] Read more.
This article presents the Network Empower and Prototyping Platform (NEP+), a flexible framework purposefully crafted to simplify the process of interactive application development, catering to both technical and non-technical users. The name "NEP+" encapsulates the platform’s dual mission: to empower the network-related capabilities of ZeroMQ and to provide software tools and interfaces for prototyping and integration. NEP+ accomplishes this through a comprehensive quality model and an integrated software ecosystem encompassing middleware, user-friendly graphical interfaces, a command-line tool, and an accessible end-user programming interface. This article primarily focuses on presenting the proposed quality model and software architecture, illustrating how they can empower developers to craft cross-platform, accessible, and user-friendly interfaces for various applications, with a particular emphasis on robotics and the Internet of Things (IoT). Additionally, we provide practical insights into the applicability of NEP+ by briefly presenting real-world user cases where human-centered projects have successfully utilized NEP+ to develop robotics systems. To further emphasize the suitability of NEP+ tools and interfaces for developer use, we conduct a pilot study that delves into usability and workload assessment. The outcomes of this study highlight the user-friendly features of NEP+ tools, along with their ease of adoption and cross-platform capabilities. The novelty of NEP+ fundamentally lies in its holistic approach, acting as a bridge across diverse user groups, fostering inclusivity, and promoting collaboration. Full article
(This article belongs to the Special Issue Intelligent Social Robotic Systems)
►▼ Show Figures

Figure 1

17 pages, 1475 KiB  
Article
Socially-Assistive Robots to Support Learning in Students on the Autism Spectrum: Investigating Educator Perspectives and a Pilot Trial of a Mobile Platform to Remove Barriers to Implementation
by David Silvera-Tawil, Susan Bruck, Yi Xiao and DanaKai Bradford
Sensors 2022, 22(16), 6125; https://doi.org/10.3390/s22166125 - 16 Aug 2022
Cited by 11 | Viewed by 3669
Abstract
Technology offers educators tools that can tailor learning to students’ learning styles and interests. Research into the use of socially-assistive robots as a learning support for children on the autism spectrum are showing promising results. However, to date, few schools have introduced these [...] Read more.
Technology offers educators tools that can tailor learning to students’ learning styles and interests. Research into the use of socially-assistive robots as a learning support for children on the autism spectrum are showing promising results. However, to date, few schools have introduced these robots to support learning in students on the autism spectrum. This paper reports on a research project that investigated the barriers to implementing socially-assistive robot supported learning, and the expectations, perceived benefits and concerns of school teachers and therapists of students on the autism spectrum and adults on the autism spectrum. First, three focus groups were conducted with six adults on the autism spectrum, and 13 teachers and therapists of students from two autism-specific schools. During the focus groups, there was cautious optimism from participants about the value of socially-assistive robots for teaching support. While the data showed that participants were in favour of trialling socially-assistive robots in the classroom, they also raised several concerns and potential barriers to implementation, including the need for teacher training. In response to their concerns, the second part of the project focussed on developing a software platform and mobile application (app) to support the introduction of robots into autism-specific classrooms. The software platform and app were then trialled in two schools (n = 7 teachers and therapists). Results from focus groups indicated that participants believe socially-assistive robots could be useful for learning support, as the mobile app provides an easy to use tool to support preparing and conducting lessons that would motivate them to trial robots in the classroom. Full article
(This article belongs to the Special Issue Intelligent Social Robotic Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop