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

Deadline for manuscript submissions: closed (10 February 2026) | Viewed by 31645

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Special Issue Editors

Dr. Guoniu Zhu

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Guest Editor

Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Interests: task and motion planning; grasping and dexterous manipulation; biologically inspired design
Special Issues, Collections and Topics in MDPI journals

Dr. Yi Fang

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Guest Editor

School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: industrial robots; motion planning and control; multi-objective intelligent optimization
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Dr. Yang Yang

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Guest Editor

Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China
Interests: creative soft robot designs; bioinspired soft robots; pneumatic soft robots; magnetic soft robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, entitled "Recent Advances in Bioinspired Robot and Intelligent Systems" in the journal Biomimetics. Bioinspired robotics has emerged as a fascinating and rapidly growing field that draws inspiration from nature to develop innovative and efficient robotic systems. By studying and mimicking the remarkable abilities of biological systems, researchers aim to create robots with enhanced performance, adaptability, and resilience. This research area holds immense potential for advancing robotic technologies and developing solutions to complex challenges in various domains, such as healthcare, environmental monitoring, search and rescue, and space exploration.

This Special Issue aims to showcase the latest advances and trends in the bioinspired design approaches for robots. We seek to compile a collection of high-quality research articles that explore novel design principles, materials, control systems, and optimization techniques inspired by biological systems. The scope of this Special Issue aligns well with the journal's focus on cutting-edge research in robotics, biomimetics, and intelligent systems. By bringing together a diverse range of contributions from experts in the field, we aim to provide a comprehensive overview of the current state of the art and future directions in bioinspired robot design.

Original research articles and reviews are welcome for this Special Issue. Research areas may include, but are not limited to, the following:

Bioinspired design principles and methodologies for robots;
Optimal design of bioinspired sensors, actuators, and control systems;
Bioinspired materials and structures for soft robotics;
Evolutionary algorithms and swarm intelligence for robot optimization;
Bioinspired locomotion and manipulation strategies;
Biohybrid systems and bioinspired human–robot interaction;
Bioinspired energy harvesting and self-healing mechanisms;
Applications of bioinspired robots in healthcare, environmental monitoring, and exploration;
Bioinspired artificial intelligence and machine learning for robot control and perception;
Bioinspired optimization techniques for robot design and performance enhancement.

We look forward to receiving your contributions to this Special Issue and working together to advance the field of bioinspired robotics.

Dr. Guoniu Zhu
Dr. Yi Fang
Dr. Yang Yang
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

bioinspired robotics
biomimetic design
biologically inspired materials
bioinspired control systems
bioinspired algorithms
soft robotics
artificial muscles
compliant mechanisms
intelligent algorithms
intelligent systems
swarm intelligence

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Related Special Issue

Optimal Design Approaches of Bioinspired Robots in Biomimetics (5 articles)

Published Papers (12 papers)

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Research

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17 pages, 4699 KB

Open AccessArticle

Interactive Teleoperation of an Articulated Robotic Arm Using Vision-Based Human Hand Tracking

by Marius-Valentin Drăgoi, Aurel-Viorel Frimu, Andrei Postelnicu, Roxana-Adriana Puiu, Gabriel Petrea and Alexandru Hank

Biomimetics 2026, 11(2), 151; https://doi.org/10.3390/biomimetics11020151 - 19 Feb 2026

Cited by 1 | Viewed by 1215

Abstract

Interactive teleoperation offers an intuitive pathway for human–robot interaction, yet many existing systems rely on dedicated sensors or wearable devices, limiting accessibility and scalability. This paper presents a vision-based teleoperation framework that enables real-time control of an articulated robotic arm (five joints plus a gripper actuator) using human hand tracking from a single, typical laptop camera. Hand pose and gesture information are extracted using a real-time landmark estimation pipeline, and a set of compact kinematic descriptors—palm position, apparent hand scale, wrist rotation, hand pitch, and pinch gesture—are mapped to robotic joint commands through a calibration-based control strategy. Commands are transmitted over a lightweight network interface to an embedded controller that executes synchronized servo actuation. To enhance stability and usability, temporal smoothing and rate-limited updates are employed to mitigate jitter while preserving responsiveness. In a human-in-the-loop evaluation with 42 participants, the system achieved an 88% success rate (37/42), with a completion time of 53.48 ± 18.51 s, a placement error of 6.73 ± 3.11 cm for successful trials (n = 37), and an ease-of-use score of 2.67 ± 1.20 on a 1–5 scale. Results indicate that the proposed approach enables feasible interactive teleoperation without specialized hardware, supporting its potential as a low-cost platform for robotic manipulation, education, and rapid prototyping. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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25 pages, 16758 KB

Open AccessArticle

Design, Modeling, and Experimental Verification of a Fully Decoupled Tendon-Driven Humanoid Arm

by Diwei Huang, Hao Li, Xiao Jiang, Jiahao Shen, Hong Luo, Chongkun Xia and Xueqian Wang

Biomimetics 2026, 11(2), 141; https://doi.org/10.3390/biomimetics11020141 - 12 Feb 2026

Viewed by 1138

Abstract

Human upper-limb movement is produced through the antagonistic action of tendons and is controlled in a joint space-oriented manner. Inspired by this functionality, a fully decoupled tendon-driven humanoid arm (FDTDH-Arm) is proposed, in which joint space decoupling is achieved at the mechanical level via humanoid antagonistic actuation and joint regulation rather than complex modeling-based compensation. To characterize the motion behavior introduced by rolling constraints, joint-level and whole-arm kinematic models are established. A prototype of the proposed arm is developed and experimentally validated. The results demonstrate effective mechanical joint space decoupling, passive joint stiffness of the same order of magnitude as that reported for the human upper limb, a mean positioning error of 0.40 mm, and rapid whole-arm motion with a maximum end effector velocity of 3.62 m/s. The proposed design provides a mechanical implementation and biomimetic solution for humanoid manipulation in human-interactive environments. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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24 pages, 6136 KB

Open AccessArticle

Design and Performance Evaluation of Biomimetic Suction Cups Inspired by the Abalone Muscular Foot

by Lingmi Wu, Yi Fang and Guoniu Zhu

Biomimetics 2026, 11(2), 118; https://doi.org/10.3390/biomimetics11020118 - 5 Feb 2026

Viewed by 804

Abstract

This study addresses the limited adsorption efficiency of traditional vacuum suction cups used in applications such as unmanned aerial vehicles (UAVs) and robotic systems. Inspired by the adhesion mechanism of the abalone muscular foot, we propose a novel suction cup design. The design incorporates optimization of the groove geometry, groove distribution, and the positioning of the sealing ring. To assess the mechanical performance of these designs, finite element analysis (FEA) is employed. A prototype exhibiting the most promising simulation results is fabricated and subjected to tensile testing. The experimental results show strong agreement with the simulation outcomes, thereby validating the accuracy and reliability of the FEA. The biomimetic suction cup demonstrates superior adsorption performance compared to the baseline design. Specifically, the maximum von Mises stress is reduced by 5.9%, the maximum pressure is increased by 498%, and the maximum frictional stress rises by 498%. Moreover, the maximum sliding distance is reduced by 38%, while the maximum total circumferential deformation is increased by 21%. This innovative design enhances the stress distribution across the bottom surface of the suction cup, mitigates inward edge contraction, and delays the communication between the inner cavity and the external environment, thereby improving the overall adsorption efficiency of the suction cup. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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19 pages, 38018 KB

Open AccessArticle

A Two-Stage Reinforcement Learning Framework for Humanoid Robot Sitting and Standing-Up

by Xisheng Jiang, Shihai Zhao, Yudi Zhu, Qingdu Li and Jianwei Zhang

Biomimetics 2025, 10(11), 783; https://doi.org/10.3390/biomimetics10110783 - 17 Nov 2025

Cited by 1 | Viewed by 3059

Abstract

In human daily-life scenarios, humanoid robots need not only to stand up smoothly but also to autonomously sit down for rest, energy management, and interaction. This capability is crucial for enhancing their autonomy and practicality. However, both sitting and standing involve complex dynamics constraints, diverse initial postures, and unstructured terrains, which make traditional hand-crafted controllers insufficient for multi-scenario demands. Reinforcement Learning (RL), with its generalization ability across high-dimensional state spaces and complex tasks, offers a promising solution for automatically generating motion control policies. Nevertheless, policies trained directly with RL often produce abrupt motions, making it difficult to balance smoothness and stability. To address these challenges, we propose a two-stage reinforcement learning framework: In the first stage, we focus on exploration and train initial policies for both sitting and standing, with relatively weak constraints on smoothness and joint safety, and without introducing noise. In the second stage, we refine the policies by tracking the motion trajectories obtained in the first stage, aiming for smoother transitions. We model the tracking problem as a bi-level optimization, where the tracking precision is dynamically adjusted based on the current tracking error, forming an adaptive curriculum mechanism. We apply this framework to a 1.7 m adult-scale humanoid robot, achieving stable execution in two representative real-world scenarios: sitting down onto a chair, stand up from a chair. Our approach provides a new perspective for the practical deployment of humanoid robots in real-world scenarios. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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27 pages, 8102 KB

Open AccessArticle

Extended Kalman Filter-Based Visual Odometry in Dynamic Environments Using Modified 1-Point RANSAC

by Jinhee Lee and Jaeyoung Kang

Biomimetics 2025, 10(10), 710; https://doi.org/10.3390/biomimetics10100710 - 20 Oct 2025

Viewed by 1750

Abstract

Visual odometry in dynamic environments is particularly challenging, as moving objects often cause incorrect data associations and large pose estimation errors. Traditional EKF-based VO methods rely on 1-point RANSAC to reject outliers under the assumption of a static world, thereby discarding dynamic landmarks as noise. However, in practice, outliers may arise not only from measurement errors but also from the motion of objects. To address this issue, we propose a modified 1-point RANSAC framework that detects dynamic objects and leverages both static and dynamic landmarks for ego-motion estimation. Inspired by adaptive strategies observed in biological vision systems, our approach integrates EKF-based state estimation with dynamic object tracking to achieve simultaneous ego-motion and object-motion estimation, improving robustness in complex and dynamic scenes. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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23 pages, 7170 KB

Open AccessArticle

PteroBot: A Forest Exploration Robot Bioinspired by Pteromyini Gliding Mechanism

by Minghao Fan, Jiayi Wang, Tianyi Liu, Ze Ren, Guoniu Zhu and Jin Ma

Biomimetics 2025, 10(10), 661; https://doi.org/10.3390/biomimetics10100661 - 1 Oct 2025

Viewed by 960

Abstract

Forests are critical ecosystems that play a fundamental role in supporting biodiversity and maintaining climate stability. However, forest monitoring and exploration present huge challenges due to the vast scale and complex terrain. This paper proposes a novel bionic robot, PteroBot, designed to support a new paradigm for forest exploration inspired by the locomotion of Pteromyini. PteroBot is capable of regulating its gliding posture via a flexible membrane, enabling low-energy and low-disturbance mobility within forest environments. An adaptive gliding control system tailored to the robot’s structure is developed and its effectiveness is validated through aerodynamic analysis, simulation, and experimental testing. Results show that under a cascaded closed-loop attitude controller, PteroBot achieves an average glide ratio of 2.02 and demonstrates controllable turning via attitude modulation. Additionally, comparative tests with UAVs demonstrate that PteroBot offers significant advantages in energy efficiency and acoustic disturbance. Experimental outcomes confirm that PteroBot offers a biologically inspired and ecologically compatible solution for forest exploration, with strong potential in applications such as environmental monitoring, habitat assessment, and covert reconnaissance. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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30 pages, 3177 KB

Open AccessArticle

A Concept for Bio-Agentic Visual Communication: Bridging Swarm Intelligence with Biological Analogues

by Bryan Starbuck, Hanlong Li, Bryan Cochran, Marc Weissburg and Bert Bras

Biomimetics 2025, 10(9), 605; https://doi.org/10.3390/biomimetics10090605 - 9 Sep 2025

Viewed by 2334

Abstract

Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological communication strategies into a generative visual language for unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-denied environments. Drawing from natural exemplars such as bee waggle dancing, white-tailed deer flagging, and peacock feather displays, we construct a configuration space that encodes visual messages through trajectories and LED patterns. A large language model (LLM), preconditioned using retrieval-augmented generation (RAG), serves as a generative translation layer that interprets perception data and produces symbolic UAV responses. Five test cases evaluate the system’s ability to preserve and adapt signal meaning through within-modality fidelity (maintaining symbolic structure in the same modality) and cross-modal translation (transferring meaning across motion and light). Covariance and eigenvalue-decomposition analysis demonstrate that this bio-agentic approach supports clear, expressive, and decentralized communication, with motion-based signaling achieving near-perfect clarity and expressiveness (0.992, 1.000), while LED-only and multi-signal cases showed partial success, maintaining high expressiveness (~1.000) but with much lower clarity (≤0.298). Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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21 pages, 4438 KB

Open AccessArticle

NeuroQ: Quantum-Inspired Brain Emulation

by Jordi Vallverdú and Gemma Rius

Biomimetics 2025, 10(8), 516; https://doi.org/10.3390/biomimetics10080516 - 7 Aug 2025

Cited by 5 | Viewed by 5886 | Correction

Abstract

Traditional brain emulation approaches often rely on classical computational models that inadequately capture the stochastic, nonlinear, and potentially coherent features of biological neural systems. In this position paper, we introduce NeuroQ a quantum-inspired framework grounded in stochastic mechanics, particularly Nelson’s formulation. By reformulating the FitzHugh–Nagumo neuron model with structured noise, we derive a Schrödinger-like equation that encodes membrane dynamics in a quantum-like formalism. This formulation enables the use of quantum simulation strategies—including Hamiltonian encoding, variational eigensolvers, and continuous-variable models—for neural emulation. We outline a conceptual roadmap for implementing NeuroQ on near-term quantum platforms and discuss its broader implications for neuromorphic quantum hardware, artificial consciousness, and time-symmetric cognitive architectures. Rather than demonstrating a working prototype, this work aims to establish a coherent theoretical foundation for future research in quantum brain emulation. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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33 pages, 5024 KB

Open AccessArticle

An Enhanced Dynamic Model of a Spatial Parallel Mechanism Receiving Direct Constraints from the Base at Two Point-Contact Higher Kinematic Pairs

by Chen Cheng, Xiaojing Yuan and Yenan Li

Biomimetics 2025, 10(7), 437; https://doi.org/10.3390/biomimetics10070437 - 3 Jul 2025

Viewed by 811

Abstract

In this paper, a biologically congruent parallel mechanism (PM) inspired by the masticatory system of human beings has been proposed to recreate complete chewing behaviours in three-dimensional space. The mechanism is featured by direct constraints from the base (DCFB) to its end effector at two higher kinematic pairs (HKPs), which greatly raise its topological complexity. Meanwhile, friction effects occur at HKPs and actuators, causing wear and then reducing motion accuracy. Regarding these, an inverse dynamic model that can raise the computational efficiency and the modelling fidelity is proposed, being prepared to be applied to realise accurate real-time motion and/or force control. In it, Euler parameters are employed to express the motions of the constrained end effector, and Newton–Euler’s law is applied, which can conveniently incorporate friction effects at both HKPs and actuators into the dynamic model. Numerical results show that the time consumption of the model using Euler parameters is only approximately 23% of that of the model using Euler angles, and friction effects significantly increase the model’s nonlinearity. Further, from the comparison between the models of the target PM and its counterpart free of DCFB, these constraints sharply raise the modelling complexity in terms of the transformation between Euler parameters and Euler angles in the end effector and the computational cost of inverse dynamics. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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20 pages, 7865 KB

Open AccessArticle

Design and Performance of a Neurosurgery Assisting Device

by Karla Nayeli Silva-Garcés, Marco Ceccarelli, Matteo Russo and Christopher René Torres-SanMiguel

Biomimetics 2025, 10(6), 345; https://doi.org/10.3390/biomimetics10060345 - 23 May 2025

Cited by 1 | Viewed by 3309

Abstract

This paper presents a new design solution for a neurosurgery-assisting device (NeurADe) based on a 3-RPS parallel kinematic mechanism. The NeurADe design employs compact linear actuators to accurately insert a cannula into specific areas of the brain. The CAD design and assembly of a prototype are discussed in this paper. The preliminary NeurADe prototype features 3D printed parts and incorporates mechanical and electrical components, which are designed for ease of use and lightweight functionality. For design validation and operational characterization, sensors measuring current, acceleration, and force data were utilized, and testing results are discussed to prove the feasibility of the proposed design. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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31 pages, 15164 KB

Open AccessArticle

Coordinated Locomotion Control for a Quadruped Robot with Bionic Parallel Torso

by Yaguang Zhu, Ao Cao, Zhimin He, Mengnan Zhou and Ruyue Li

Biomimetics 2025, 10(5), 335; https://doi.org/10.3390/biomimetics10050335 - 20 May 2025

Cited by 2 | Viewed by 2436

Abstract

This paper presents the design and control of a quadruped robot equipped with a six-degree-of-freedom (6-DOF) bionic active torso based on a parallel mechanism. Inspired by the compliant and flexible torsos of quadrupedal mammals, the proposed torso structure enhances locomotion performance by enabling coordinated motion between the torso and legs. A complete kinematic model of the bionic torso and the whole body of the quadruped robot is developed. To address the variation in inertial properties caused by torso motion, a model predictive control (MPC) strategy with a variable center of mass (CoM) is proposed for integrated whole-body motion control. Comparative simulations under trot gait are conducted between rigid-torso and active-torso configurations. Results show that the active torso significantly improves gait flexibility, postural stability, and locomotion efficiency. This study provides a new approach to enhancing biomimetic locomotion in quadruped robots through active torso-leg coordination. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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Review

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39 pages, 5277 KB

Open AccessReview

AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions

by Hoejin Jung, Soyoon Park, Sunghoon Joe, Sangyoon Woo, Wonchil Choi and Wongyu Bae

Biomimetics 2025, 10(7), 460; https://doi.org/10.3390/biomimetics10070460 - 14 Jul 2025

Cited by 8 | Viewed by 6059

Abstract

Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems. Full article

(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)

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