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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 June 2025 | Viewed by 1089

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

Related Special Issue

Published Papers (2 papers)

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Research

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
Viewed by 606
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 359
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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