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Biomimetics
Special Issues
Advanced Human–Robot Interaction Challenges and Opportunities
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Advanced Human–Robot Interaction Challenges and Opportunities

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Locomotion and Bioinspired Robotics".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 4342

Special Issue Editors

Dr. Qing Gao

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: deep learning; machine vision; human-robot interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuquan Leng

E-Mail Website
Guest Editor
School of Biomedical Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: human-robot interaction; exoskeleton robot
Dr. Xin Zhang

E-Mail Website
Guest Editor
School of Electrical and Mechanical Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK
Interests: human-robot interaction; space robot
Dr. Dongxu Gao

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
Interests: medical imaging; human-robot interaction; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advances in Human-Robot Interaction (HRI), soliciting research on state-of-the-art interaction modalities including hand gesture, body action, speech, gaze, and facial expression. It investigates the refinement and integration of these modalities to create more natural, intuitive, and effective interactions between humans and robots. The issue covers a broad spectrum of applications, from precise teleoperation and human-robot collaborative manipulation in shared workspaces to socially assistive robotics and emotional companionship. Contributions address key challenges in perception, adaptive control, cognitive modeling, and real-world deployment, offering insights into the future of seamless and trustworthy human-robot partnerships.

Dr. Qing Gao
Prof. Dr. Yuquan Leng
Dr. Xin Zhang
Dr. Dongxu Gao
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 250 words) can be sent to the Editorial Office for assessment.

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. Biomimetics is an international peer-reviewed open access monthly 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 2200 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
  • hand gesture
  • body pose/action
  • facial expression
  • gaze tracking
  • teleoperation
  • collaborative
  • robotics
  • multimodal interaction

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Published Papers (5 papers)

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Research

12 pages, 2015 KB  
Communication
Synthetic Data-Driven Exoskeleton Control via Contralateral Gait Fusion for Variable-Speed Walking
by Jingshu Shi, Hongwu Zhu, Yifei Yang, Bowen Liu and Xingjun Wang
Biomimetics 2026, 11(5), 319; https://doi.org/10.3390/biomimetics11050319 - 3 May 2026
Viewed by 717
Abstract
Data-driven exoskeletons offer the potential for adaptive augmentation of human mobility. Yet their widespread adoption is hindered by labor-intensive biomechanical data collection and manual tuning. Herein, this study presents a highly efficient synthetic data approach to facilitate data-driven pipelines. We leveraged an Adversarial [...] Read more.
Data-driven exoskeletons offer the potential for adaptive augmentation of human mobility. Yet their widespread adoption is hindered by labor-intensive biomechanical data collection and manual tuning. Herein, this study presents a highly efficient synthetic data approach to facilitate data-driven pipelines. We leveraged an Adversarial Motion Priors (AMP) agent to learn stylized walking within a massively parallel, physics-based simulation. The resulting high-fidelity data were collected and validated against OpenSim inverse dynamics pipelines. Further, we trained an end-to-end torque prediction algorithm using the collected data. A novel CNN-Transformer architecture was developed to map contralateral swing-phase data to variable-length push-off torque profiles. This enabled real-time, adaptive torque assistance of exoskeletons for variable-speed walking. A custom ankle exoskeleton was used to demonstrate robust sim-to-real transferability. Our system achieved an average root mean square error of approximately 0.081 ± 0.015 newton-meters per kilogram and an average R2 of 0.836 ± 0.050 across speeds ranging from 0.6 to 1.75 m·s−1. The controller significantly reduced user-positive ankle mechanical work by up to 14 ± 6.30%. Finally, our multi-sensor configuration exhibited inherent fault tolerance, ensuring safe operation even under partial sensor failure. By taking a scalable, data-driven approach, this work offers a practical pathway toward deploying autonomous exoskeletons in versatile, real-world environments. Full article
(This article belongs to the Special Issue Advanced Human–Robot Interaction Challenges and Opportunities)
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20 pages, 2724 KB  
Article
An Efficient Multi-Channel Electrotactile Parameter Configuration Method for Personalized Teleoperation
by Kaicheng Zhang, Kairu Li, Peiyao Wang and Yixuan Sheng
Biomimetics 2026, 11(5), 310; https://doi.org/10.3390/biomimetics11050310 - 1 May 2026
Viewed by 577
Abstract
Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with [...] Read more.
Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed 3 × 2 multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson r = 0.48, p < 0.05). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration. Full article
(This article belongs to the Special Issue Advanced Human–Robot Interaction Challenges and Opportunities)
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35 pages, 13771 KB  
Article
BioLAMR: A Biomimetically Inspired Large Language Model Adaptation Framework for Automatic Modulation Recognition
by Yubo Mao, Wei Xu, Jijia Sang and Haoan Liu
Biomimetics 2026, 11(4), 288; https://doi.org/10.3390/biomimetics11040288 - 21 Apr 2026
Viewed by 597
Abstract
Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) [...] Read more.
Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) are not natively compatible with continuous I/Q signals due to the inherent modality gap. We propose BioLAMR, a GPT-2 adaptation framework for AMR inspired by the auditory system’s parallel time–frequency processing and cortical hierarchy. The framework combines bio-inspired dual-domain feature extraction with parameter-efficient LLM adaptation. BioLAMR includes three components. First, a lightweight dual-domain fusion (LDDF) module extracts complementary time- and frequency-domain features and fuses them through channel and spatial attention. Second, a convolutional embedding module converts continuous I/Q signals into GPT-2-compatible sequences without discrete tokenization. Third, a hierarchical fine-tuning strategy updates only 8.9% of parameters to preserve pretrained knowledge while adapting to modulation recognition. Experiments on the RadioML2016.10a and RadioML2016.10b benchmarks show that BioLAMR achieves overall accuracies of 64.99% and 67.43%, outperforming the strongest competing method by 2.60 and 2.47 percentage points, respectively. Under low-SNR conditions, it reaches 36.78% and 38.14%, the best results among the compared methods. Ablation studies verify the contribution of each component. These results demonstrate that combining dual-domain signal modeling with parameter-efficient GPT-2 adaptation is an effective route to robust AMR in challenging wireless environments. Full article
(This article belongs to the Special Issue Advanced Human–Robot Interaction Challenges and Opportunities)
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27 pages, 3300 KB  
Article
A Methodology for Evaluating User Experience in Human-Centered Extended Reality Applications
by Daniela Quiñones, Luis Felipe Rojas, Renato Olavarría, Claudio Cubillos and Felipe Muñoz-La Rivera
Biomimetics 2026, 11(3), 182; https://doi.org/10.3390/biomimetics11030182 - 3 Mar 2026
Viewed by 1169
Abstract
Extended Reality (XR) technologies are increasingly used to create immersive and interactive systems across domains such as education, training, health, and entertainment. As these systems become more complex and multisensory, evaluating user experience (UX) in XR environments requires approaches that go beyond traditional [...] Read more.
Extended Reality (XR) technologies are increasingly used to create immersive and interactive systems across domains such as education, training, health, and entertainment. As these systems become more complex and multisensory, evaluating user experience (UX) in XR environments requires approaches that go beyond traditional usability assessments and consider perceptual, cognitive, emotional, and interaction-related factors. However, existing UX evaluation efforts in XR often rely on isolated instruments or domain-specific studies, lacking a systematic and reusable evaluation methodology. This paper proposes a human-centered methodology for evaluating user experience in extended reality applications, integrating UX dimensions and XR-specific characteristics into a structured and coherent evaluation process. The methodology is grounded in a multi-phase research process that includes a comprehensive literature review, expert consultation, correlation analysis between UX dimensions and XR features, and formal specification of evaluation phases and activities. Based on this process, the proposed methodology supports evaluators in selecting appropriate UX evaluation methods and instruments according to the characteristics and experiential goals of XR applications. The methodology defines a set of UX dimensions tailored to immersive environments, capturing perceptual, cognitive, emotional, and interaction aspects that are critical for the design and evaluation of adaptive and human-centered XR systems. An expert-based validation was conducted to assess the clarity, usefulness, and applicability of the methodology, leading to refinements in its structure and descriptions. The methodology promotes a human-centered approach by considering user perception, emotional impact, and contextual experience across XR modalities. It additionally contributes to the field by offering a reusable process for UX evaluation in XR, supporting more consistent, transparent, and human-centered assessment practices. It also provides a foundation for future empirical studies and the development of evaluation approaches inspired by natural and adaptive human–environment interactions. Full article
(This article belongs to the Special Issue Advanced Human–Robot Interaction Challenges and Opportunities)
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20 pages, 2541 KB  
Article
Space Human–Robot Interaction with Gaze Tracking Based on Attention Mechanism
by Lihong Dai, Jinguo Liu and Zhaojie Ju
Biomimetics 2026, 11(2), 103; https://doi.org/10.3390/biomimetics11020103 - 2 Feb 2026
Viewed by 843
Abstract
Gaze is a natural and rapid non-verbal interaction mode, particularly well-suited for human–robot interaction in busy space environments. However, the space human–robot interaction based on gaze is still in its infancy. Therefore, this paper conducts a preliminary exploration in this area. Using the [...] Read more.
Gaze is a natural and rapid non-verbal interaction mode, particularly well-suited for human–robot interaction in busy space environments. However, the space human–robot interaction based on gaze is still in its infancy. Therefore, this paper conducts a preliminary exploration in this area. Using the AAR-2, a free-flying astronaut-assistant robot, as the platform, we establish a gaze tracking database, construct a gaze tracking model based on an attention mechanism, and develop a human–robot interface. When the astronaut gazes at a control button on the interface, the corresponding control instruction is transmitted to the STM32 controller within the AAR-2 via radio frequency communication. Subsequently, the AAR-2 is propelled by ducted fans to perform the corresponding action. At the same time, the AAR-2 feeds back its operational state to the astronaut, thereby enabling space human–robot interaction. In the system, we achieve an effective gaze tracking model with high accuracy and implement an efficient image preprocessing method with high real-time performance. The experimental results demonstrate that the system can meet the actual requirements for accuracy and real-time processing. Full article
(This article belongs to the Special Issue Advanced Human–Robot Interaction Challenges and Opportunities)
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