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Biomimetics
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Biomimetic Innovations for Human–Machine Interaction
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Biomimetic Innovations for Human–Machine Interaction

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biomimetic Design, Constructions and Devices".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2889

Special Issue Editor

Dr. Hang Su

E-Mail Website
Guest Editor
Laboratory IBISC, Paris-Saclay University, Paris, France
Interests: robotics; human-robot interaction; deep learning; bilateral teleoperation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The biomimetic paradigm is shaping a pivotal revolution in human–machine interaction, especially when intersected with advanced disciplines like robotics, mechatronics, and cyborg intelligence.

This Special Issue primarily focuses on the biomimetic aspects that are driving innovations in human–machine interaction. It aims to explore how natural systems inspire advanced modeling, sensory perception, adaptive control, and decision-making mechanisms in robotics and mechatronics. Furthermore, it will delve into how these biomimetic principles can elevate the adaptiveness and autonomy of systems, including those in the realm of cyborg intelligence.

Potential topics include, but are not limited to, the following:

  • Biomimetic sensors;
  • Perception systems in robotics and mechatronics;
  • AI algorithms inspired by natural cognitive processes in cyborg intelligence;
  • Biomimetic multimodal interactive interface;
  • Biomimetic control algorithms for adaptive and autonomous systems;
  • Data-driven biomimetic models in kinematics and dynamics;
  • Ethical considerations in biomimetic human–computer interactions and cyborg intelligence;
  • Human-centered biomimetic systems for enhanced adaptability and robustness;
  • VR/AR simulators with biomimetic elements for robotics and mechatronics.

Dr. Hang Su
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. 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

  • biomimetic aspects
  • human–machine interaction
  • human–computer interaction

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Related Special Issue

Published Papers (4 papers)

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Research

20 pages, 4620 KiB  
Article
An Interactive Human-in-the-Loop Framework for Skeleton-Based Posture Recognition in Model Education
by Jing Shen, Ling Chen, Xiaotong He, Chuanlin Zuo, Xiangjun Li and Lin Dong
Biomimetics 2025, 10(7), 431; https://doi.org/10.3390/biomimetics10070431 - 1 Jul 2025
Viewed by 336
Abstract
This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are reserved for testing across five core posture categories: [...] Read more.
This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are reserved for testing across five core posture categories: standing, sitting, jumping, crouching, and lying. From each image, comprehensive skeletal features are extracted, including joint coordinates, angles, limb lengths, and symmetry metrics. Multiple classification algorithms—traditional (KNN, SVM, Random Forest) and deep learning-based (LSTM, Transformer)—are compared to identify effective combinations of features and models. Experimental results show that deep learning models achieve superior accuracy on complex postures, while traditional models remain competitive with low-dimensional features. Beyond classification, the system integrates posture recognition with a visual recommendation module. Recognized poses are used to retrieve matched examples from a reference library, allowing instructors to browse and select posture suggestions for learners. This semi-automated feedback loop enhances teaching interactivity and efficiency. Among all evaluated methods, the Transformer model achieved the best accuracy of 92.7% on the dataset, demonstrating the effectiveness of our closed-loop framework in supporting pose classification and model training. The proposed framework contributes both algorithmic insights and a novel application design for posture-driven educational support systems. Full article
(This article belongs to the Special Issue Biomimetic Innovations for Human–Machine Interaction)
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19 pages, 564 KiB  
Article
Technology Acceptance and Usability of a Therapy System with a Humanoid Robot Serving as Therapeutic Assistant for Post-Stroke Arm and Neurovisual Rehabilitation—An Evaluation Based on Stroke Survivors’ Experience
by Thomas Platz, Alexandru-Nicolae Umlauft, Ann Louise Pedersen and Peter Forbrig
Biomimetics 2025, 10(5), 289; https://doi.org/10.3390/biomimetics10050289 - 4 May 2025
Viewed by 557
Abstract
Background: This study performed an evaluation of technology acceptance of the therapeutic system E-BRAiN (Evidence-Based Robot Assistance in Neurorehabilitation) by stroke survivors receiving therapy with the system. Methods: The evaluation was based on a 49-item questionnaire addressing technology acceptance (I) with its constituents, [...] Read more.
Background: This study performed an evaluation of technology acceptance of the therapeutic system E-BRAiN (Evidence-Based Robot Assistance in Neurorehabilitation) by stroke survivors receiving therapy with the system. Methods: The evaluation was based on a 49-item questionnaire addressing technology acceptance (I) with its constituents, i.e., perceived usefulness, perceived ease of use, perceived adaptability, perceived enjoyment, attitude, trust, anxiety, social influence, perceived sociability, and social presence (41 items), and (II) more general items exploring user experience in terms of both technology acceptance (3 items) and usability (5 open-question items). Results: Eleven consecutive sub-acute stroke survivors who had received either arm rehabilitation sessions (n = 5) or neglect therapy (n = 6) led by a humanoid robot participated. The multidimensional “strength of acceptance” summary statistic (Part I) indicates a high degree of technology acceptance (mean, 4.0; 95% CI, 3.7 to 4.3), as does the “general acceptance” summary statistic (mean, 4.1; 95% CI, 3.3 to 4.9) (art II) (scores ranging from 1, lowest degree of acceptance, to 5, highest degree of acceptance, with a score of 3 as neutral experience anchor). Positive ratings were also documented for all assessed constituents (Part I), as well as the perception that it makes sense to use the robot technology for stroke therapy and as a supplement for users’ own therapy (Part II). Conclusions: A high degree of technology acceptance and its constituents, i.e., perceived functionality and social behaviour of the humanoid robot and own emotions while using the system, could be corroborated among stroke survivors who used the therapeutic system E-BRAiN. Full article
(This article belongs to the Special Issue Biomimetic Innovations for Human–Machine Interaction)
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19 pages, 28961 KiB  
Article
Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception
by Wen Qi, Haoyu Fan, Cankun Zheng, Hang Su and Samer Alfayad
Biomimetics 2025, 10(3), 186; https://doi.org/10.3390/biomimetics10030186 - 18 Mar 2025
Cited by 2 | Viewed by 1191
Abstract
Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping [...] Read more.
Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping intuition through operator-worn gloves with tactile-guided reinforcement learning. The framework’s key innovation lies in its Tactile-Driven DCNN architecture—a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns—coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. Experimental results demonstrate reliable grasping performance across deformable and rigid objects while maintaining force precision critical for fragile targets. By bridging human teleoperation with autonomous tactile adaptation, RLMP eliminates dependency on visual input and predefined object models, establishing a new paradigm for robotic dexterity in occlusion-rich scenarios. Full article
(This article belongs to the Special Issue Biomimetic Innovations for Human–Machine Interaction)
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15 pages, 2125 KiB  
Article
Neuromusculoskeletal Control for Simulated Precision Task versus Experimental Data in Trajectory Deviation Analysis
by Jean Mendes Nascimento, Camila Taira, Eric Cito Becman and Arturo Forner-Cordero
Biomimetics 2025, 10(3), 138; https://doi.org/10.3390/biomimetics10030138 - 25 Feb 2025
Viewed by 466
Abstract
Control remains a challenge in precision applications in robotics, particularly when combined with execution in small time intervals. This study employed a two-degree-of-freedom (2-DoF) planar robotic arm driven by a detailed human musculoskeletal model for actuation, incorporating nonlinear control techniques to execute a [...] Read more.
Control remains a challenge in precision applications in robotics, particularly when combined with execution in small time intervals. This study employed a two-degree-of-freedom (2-DoF) planar robotic arm driven by a detailed human musculoskeletal model for actuation, incorporating nonlinear control techniques to execute a precision task through simulation. Then, we compared these simulations with real experimental data from healthy subjects performing the same task. Our results show that the Feedback Linearization Control (FLC) applied performed satisfactorily within the task execution constraints compared to a robust nonlinear control technique, i.e., Sliding Mode Control (SMC). On the other hand, differences can be observed between the behavior of the simulated model and the real experimental data, where discrepancies in terms of errors were found. The model errors increased with the amplitude and remained unchanged with any increase in the task execution frequency. However, in human trials, the errors increased both with the amplitude and, notably, with a drastic rise in frequency. Full article
(This article belongs to the Special Issue Biomimetic Innovations for Human–Machine Interaction)
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