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Human-Robot Interaction in Intelligent Robotics
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Human-Robot Interaction in Intelligent Robotics

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1867

Special Issue Editor

Dr. Vazquez Gomez Juan Ignacio

E-Mail Website
Guest Editor
Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
Interests: telecommunications; IoT; deep reinforcement learning (DRL); multi-agent reinforcement learning (MARL); artificial intelligence

Special Issue Information

Dear Colleagues,

As robots become smarter, by incorporating increasingly advanced artificial intelligence techniques, including generative approaches, they are also able to exhibit novel and sophisticated interaction strategies with humans. This Special Issue will focus on the intersection of robotics, artificial intelligence, and human-robot interactions, covering topics such as the following:

  • Perception of human feelings using robots;
  • Reinforcement learning for human-robot interactions;
  • Generative AI for human-robot interactions;
  • Multi-robot collaboration with humans;
  • Virtual, extended, and augmented reality in human-robot interactions;
  • Human acceptance of smarter robots;
  • Explainable AI and transparency in human-robot interactions;
  • User experience design for interactive intelligent robots;
  • Emotional intelligence in robotic systems;
  • Social implications of robots with advanced AI capabilities;
  • Human-robot collaboration in industrial settings;
  • Trust-building mechanisms in human-robot relationships;
  • Cognitive models for understanding human behavior among robots;
  • Security and privacy concerns in AI-driven robotic systems;
  • Integrating natural language processing in cognitive robotics;
  • Robotic companionship and emotional support.

Dr. Vazquez Gomez Juan Ignacio
Guest Editor

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

  • human–robot interaction
  • artificial intelligence
  • robotics
  • reinforcement learning
  • human–robot collaboration
  • extended and augmented reality
  • generative artificial intelligence
  • emotional robots

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Published Papers (1 paper)

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Research

19 pages, 4674 KiB  
Article
Reducing Cross-Sensor Domain Gaps in Tactile Sensing via Few-Sample-Driven Style-to-Content Unsupervised Domain Adaptation
by Xingshuo Jing and Kun Qian
Sensors 2025, 25(1), 256; https://doi.org/10.3390/s25010256 - 5 Jan 2025
Viewed by 1267
Abstract
Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the [...] Read more.
Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the data collected from each type of visuotactile sensors as domains, we propose a few-sample-driven style-to-content unsupervised domain adaptation method to reduce cross-sensor domain gaps. We first propose a Global and Local Aggregation Bottleneck (GLAB) layer to compress features extracted by an encoder, enabling the extraction of features containing key information and facilitating unlabeled few-sample-driven learning. We introduce a Fourier-style transformation (FST) module and a prototype-constrained learning loss to promote global conditional domain-adversarial adaptation, bridging style-level gaps. We also propose a high-confidence guided teacher–student network, utilizing a self-distillation mechanism to further reduce content-level gaps between the two domains. Experiments on three cross-sensor domain adaptation and real-world robotic cross-sensor shape recognition tasks demonstrate that our method outperforms state-of-the-art approaches, particularly achieving 89.8% accuracy on the DIGIT recognition dataset. Full article
(This article belongs to the Special Issue Human-Robot Interaction in Intelligent Robotics)
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