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Biomimetics
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Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition
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Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3331

Special Issue Editors

Dr. Pengcheng Liu

E-Mail Website
Guest Editor
Department of Computer Science, University of York, Heslington YO10 5GH, UK
Interests: robot learning; applied control; bioinspiration and biomimetics
Special Issues, Collections and Topics in MDPI journals
Dr. Qinbing Fu

E-Mail Website
Guest Editor
Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, China
Interests: brain-inspired intelligence; motion perception; machine vision; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Tiong Hoo Lim

E-Mail Website
Guest Editor
Faculty of Engineering, Universiti Teknologi Brunei, Mukim Gadong A BE1410, Brunei
Interests: artificial intelligence; autonomous systems; and embedded technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotics is a multidisciplinary research field that demonstrates enormous potential. It concerns developing intelligent robotic systems that are capable of making decisions and acting autonomously in real and dynamic environments to accomplish tasks and assist humans in the betterment of society. Recently, advances in the computational study of intelligent behaviors such as learning and adaptation have led to powerful insights about the nature of learning in both humans, animals, materials, and machines. However, new and challenging theoretical and technological problems are being posed. One can apply the computational metaphor in different ways, and computational learning has become an important topic within many paradigms, including artificial intelligence, pattern recognition, control theory, cognitive intelligence, behavioral intelligence, and statistics. Such a convergence of interests is encouraging, but few researchers in this active area communicate across disciplinary boundaries and even fewer are skilled in the ‘language’ and techniques of more than one approach. With this new era of computational learning for robotics, further research is needed to advance the field and to evaluate multidisciplinary concerns regarding learning and adaptation techniques.

The aim of this Special Issue is to highlight the roles of advanced bio-inspired and biomimetic intelligence for robotics applications and prior knowledge in achieving successes, especially how they contribute to taming the complexity of related domains. This includes but is not limited to the following topics:

  • Behavioral and biological learning and control;
  • Computational neuroscience;
  • Cognitive robotics and computation;
  • Evolutionary robotics, multi-robot systems, and swarm intelligence;
  • Computational modeling of biological systems;
  • Biomechanics, biomechatronics, and bioengineering;
  • Smart materials;
  • Soft robotics and sensing;
  • Human‒robot interaction and collaboration;
  • Bio-inspired approaches for robot design, control, and optimization;
  • Morphological computation and embodied intelligence;
  • Bio-inspired spiking neural networks;
  • Bio-inspired vision systems;
  • Imitation learning, Bayesian/probabilistic learning;
  • Bio-inspired legged robotics;
  • Bio-inspired/biomimetic underwater robotics;
  • Micro- and Nano-robotics;
  • Healthcare and rehabilitation;
  • Flexible electronics and piezoelectric actuators.

Dr. Pengcheng Liu
Dr. Qinbing Fu
Dr. Tiong Hoo Lim
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. 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

  • biological inspiration
  • biomimetics
  • computational learning
  • robotics

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Related Special Issue

Published Papers (3 papers)

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Research

29 pages, 9718 KiB  
Article
Segment, Compare, and Learn: Creating Movement Libraries of Complex Task for Learning from Demonstration
by Adrian Prados, Gonzalo Espinoza, Luis Moreno and Ramon Barber
Biomimetics 2025, 10(1), 64; https://doi.org/10.3390/biomimetics10010064 - 17 Jan 2025
Viewed by 699
Abstract
Motion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for each task to [...] Read more.
Motion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for each task to be learned. To address this challenge, this work presents an algorithm for acquiring robotic skills through automatic and unsupervised segmentation. The algorithm divides tasks into simpler subtasks and generates motion primitive libraries that group common subtasks for use in subsequent learning processes. Our algorithm is based on an initial segmentation step using a heuristic method, followed by probabilistic clustering with Gaussian Mixture Models. Once the segments are obtained, they are grouped using Gaussian Optimal Transport on the Gaussian Processes (GPs) of each segment group, comparing their similarities through the energy cost of transforming one GP into another. This process requires no prior knowledge, it is entirely autonomous, and supports multimodal information. The algorithm enables generating trajectories suitable for robotic tasks, establishing simple primitives that encapsulate the structure of the movements to be performed. Its effectiveness has been validated in manipulation tasks with a real robot, as well as through comparisons with state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition)
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15 pages, 10958 KiB  
Article
ARS: AI-Driven Recovery Controller for Quadruped Robot Using Single-Network Model
by Han Sol Kang, Hyun Yong Lee, Ji Man Park, Seong Won Nam, Yeong Woo Son, Bum Su Yi, Jae Young Oh, Jun Ha Song, Soo Yeon Choi, Bo Geun Kim, Hyun Seok Kim and Hyouk Ryeol Choi
Biomimetics 2024, 9(12), 749; https://doi.org/10.3390/biomimetics9120749 - 10 Dec 2024
Viewed by 828
Abstract
Legged robots, especially quadruped robots, are widely used in various environments due to their advantage in overcoming rough terrains. However, falling is inevitable. Therefore, the ability to overcome a falling state is an essential ability for legged robots. In this paper, we propose [...] Read more.
Legged robots, especially quadruped robots, are widely used in various environments due to their advantage in overcoming rough terrains. However, falling is inevitable. Therefore, the ability to overcome a falling state is an essential ability for legged robots. In this paper, we propose a method to fully recover a quadruped robot from a fall using a single-neural network model. The neural network model is trained in two steps in simulations using reinforcement learning, and then directly applied to AiDIN-VIII, a quadruped robot with 12 degrees of freedom. Experimental results using the proposed method show that the robot can successfully recover from a fall within 5 s in various postures, even when the robot is completely turned over. In addition, we can see that the robot successfully recovers from a fall caused by a disturbance. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition)
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22 pages, 20719 KiB  
Article
A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition
by Guangxuan Gao, Renyuan Liu, Mengying Wang and Qinbing Fu
Biomimetics 2024, 9(11), 650; https://doi.org/10.3390/biomimetics9110650 - 22 Oct 2024
Viewed by 1092
Abstract
Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized [...] Read more.
Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized collision sensors across various scenes. Recent progress in neuroscience has revealed the energetic advantages of dendritic arrangements presynaptic to the LGMDs, which receive contrast polarity-specific signals on separate dendritic fields. Specifically, feed-forward inhibitory inputs arise from parallel ON/OFF pathways interacting with excitation. However, none of the previous research has investigated the evolution of a computational LGMD model with feed-forward inhibition (FFI) separated by opposite polarity. This study fills this vacancy by presenting an optimized neuronal model where FFI is divided into ON/OFF channels, each with distinct synaptic connections. To align with the energy efficiency of biological systems, we introduce an activation function associated with neural computation of FFI and interactions between local excitation and lateral inhibition within ON/OFF channels, ignoring non-active signal processing. This approach significantly improves the time efficiency of the LGMD model, focusing only on substantial luminance changes in image streams. The proposed neuronal model not only accelerates visual processing in relatively stationary scenes but also maintains robust selectivity to ON/OFF-contrast looming stimuli. Additionally, it can suppress translational motion to a moderate extent. Comparative testing with state-of-the-art based on ON/OFF channels was conducted systematically using a range of visual stimuli, including indoor structured and complex outdoor scenes. The results demonstrated significant time savings in silico while retaining original collision selectivity. Furthermore, the optimized model was implemented in the embedded vision system of a micro-mobile robot, achieving the highest success ratio of collision avoidance at 97.51% while nearly halving the processing time compared with previous models. This highlights a robust and parsimonious collision-sensing mode that effectively addresses real-world challenges. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition)
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