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Biomimetics
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Theory and Application of Bioinspired Robotics and Intelligent Control
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Theory and Application of Bioinspired Robotics and Intelligent Control

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 2026 | Viewed by 4239

Special Issue Editors

Dr. Yuchuang Tong

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Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100089, China
Interests: embodied intelligence; humanoid robots; robotic intelligent control; robotic learning
Dr. Pengyuan Zhao

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Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: space robotics; mechanism and robotics; deployable structures

Special Issue Information

Dear Colleagues,

This Special Issue presents recent advances in the theory and application of bioinspired robotics and intelligent control, focusing on dexterous manipulation, human–robot interaction, locomotion control, coordinated locomotion manipulation and whole-body control. Inspired by biological systems, these robots integrate adaptive control, multi-modal perception and learning-based strategies to achieve robust, flexible and generalizable behaviors in unstructured environments. Core studies emphasize embodied approaches that tightly couple sensing, actuation and control, to enhance dexterity, stability and task adaptability. Applications span industrial automation, service robotics, medical assistance and complex environment exploration, demonstrating how bioinspired principles enable practical and efficient robotic solutions. Collectively, this collection highlights the synergy between biological inspiration and intelligent control, advancing both theoretical understanding and the real-world deployment of autonomous embodied robotic systems.

Dr. Yuchuang Tong
Dr. Pengyuan Zhao
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
  • intelligent control
  • dexterous manipulation
  • locomotion control
  • locomotion-manipulation coordination

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Published Papers (5 papers)

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Research

16 pages, 1529 KB  
Article
Image Segmentation-Guided Visual Tracking on a Bio-Inspired Quadruped Robot
by Hewen Xiao, Guangfu Ma and Weiren Wu
Biomimetics 2026, 11(4), 234; https://doi.org/10.3390/biomimetics11040234 - 2 Apr 2026
Viewed by 581
Abstract
Bio-inspired quadrupedal robots exhibit superior adaptability and mobility in unstructured environments, making them suitable for complex task scenarios such as navigation, obstacle avoidance, and tracking in a variety of environments. Visual perception plays a critical role in enabling autonomous behavior, offering a cost-effective [...] Read more.
Bio-inspired quadrupedal robots exhibit superior adaptability and mobility in unstructured environments, making them suitable for complex task scenarios such as navigation, obstacle avoidance, and tracking in a variety of environments. Visual perception plays a critical role in enabling autonomous behavior, offering a cost-effective alternative to multi-sensor systems. This paper proposes an image segmentation-guided visual tracking framework to enhance both perception and motion control in quadruped robots. On the perception side, a cascaded convolutional neural network is introduced, integrating a global information guidance module to fuse low-level textures and high-level semantic features. This architecture effectively addresses limitations in single-scale feature extraction and improves segmentation accuracy under visually degraded conditions. On the control side, segmentation outputs are embedded into a biologically inspired central pattern generator (CPG), enabling coordinated generation of limb and spinal trajectories. This integration facilitates a closed-loop visual-motor system that adapts dynamically to environmental changes. Experimental evaluations on benchmark image segmentation datasets and robotic locomotion tasks demonstrate that the proposed framework achieves enhanced segmentation precision and motion flexibility, outperforming existing methods. The results highlight the effectiveness of vision-guided control strategies and their potential for deployment in real-time robotic navigation. Full article
(This article belongs to the Special Issue Theory and Application of Bioinspired Robotics and Intelligent Control)
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26 pages, 5487 KB  
Article
Global Path Planning for Land–Air Amphibious Biomimetic Robot Based on Improved PPO
by Weilai Jiang, Jingwei Liu, Wei Wang and Yaonan Wang
Biomimetics 2026, 11(1), 25; https://doi.org/10.3390/biomimetics11010025 - 1 Jan 2026
Cited by 3 | Viewed by 702
Abstract
To address the path planning challenges for land–air amphibious biomimetic robots in unstructured environments, this study proposes a global path planning algorithm based on an Improved Proximal Policy Optimization (IPPO) framework. Unlike traditional single-domain navigation, amphibious robots face significant kinematic discontinuities when switching [...] Read more.
To address the path planning challenges for land–air amphibious biomimetic robots in unstructured environments, this study proposes a global path planning algorithm based on an Improved Proximal Policy Optimization (IPPO) framework. Unlike traditional single-domain navigation, amphibious robots face significant kinematic discontinuities when switching between terrestrial and aerial modes. To mitigate this, we integrate a Gated Recurrent Unit (GRU) module into the policy network, enabling the agent to capture temporal dependencies and make smoother decisions during mode transitions. Furthermore, to enhance exploration efficiency and stability, we replace the standard Gaussian noise with Ornstein–Uhlenbeck (OU) noise, which generates temporally correlated actions aligned with the robot’s physical inertia. Additionally, a Multi-Head Self-Attention mechanism is introduced to the value network, allowing the agent to dynamically prioritize critical environmental features—such as narrow obstacles—over irrelevant background noise. The simulation results demonstrate that the proposed IPPO algorithm significantly outperforms standard PPO baselines, achieving higher convergence speed, improved path smoothness, and greater success rates in complex amphibious scenarios. Full article
(This article belongs to the Special Issue Theory and Application of Bioinspired Robotics and Intelligent Control)
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33 pages, 13758 KB  
Article
Bioinspired Simultaneous Learning and Motion–Force Hybrid Control for Robotic Manipulators Under Multiple Constraints
by Yuchuang Tong, Haotian Liu and Zhengtao Zhang
Biomimetics 2025, 10(12), 841; https://doi.org/10.3390/biomimetics10120841 - 15 Dec 2025
Cited by 2 | Viewed by 669
Abstract
Inspired by the adaptive flexible motion coordination of biological systems, this study presents a bioinspired control strategy that enables robotic manipulators to achieve precise and compliant motion–force coordination for embodied intelligence and dexterous interaction in physically constrained environments. To this end, a learning-based [...] Read more.
Inspired by the adaptive flexible motion coordination of biological systems, this study presents a bioinspired control strategy that enables robotic manipulators to achieve precise and compliant motion–force coordination for embodied intelligence and dexterous interaction in physically constrained environments. To this end, a learning-based motion–force hybrid control (LMFC) framework is proposed, which unifies learning and kinematic-level control to regulate both motion and interaction forces under incomplete or implicit kinematic information, thereby enhancing robustness and precision. The LMFC formulation recasts motion–force coordination as a time-varying quadratic programming (TVQP) problem, seamlessly incorporating multiple practical constraints—including joint limits, end-effector orientation maintenance, and obstacle avoidance—at the acceleration level, while determining control decisions at the velocity level. An RNN-based controller is further designed to integrate adaptive learning and control, enabling online estimation of uncertain kinematic parameters and mitigating joint drift. Simulation and experimental results demonstrate the effectiveness and practicality of the proposed framework, highlighting its potential for adaptive and compliant robotic control in constraint-rich environments. Full article
(This article belongs to the Special Issue Theory and Application of Bioinspired Robotics and Intelligent Control)
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14 pages, 2239 KB  
Article
Energy-Efficient Path Planning for Snake Robots Using a Deep Reinforcement Learning-Enhanced A* Algorithm
by Yang Gu, Zelin Wang and Zhong Huang
Biomimetics 2025, 10(12), 826; https://doi.org/10.3390/biomimetics10120826 - 10 Dec 2025
Cited by 2 | Viewed by 790
Abstract
Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. [...] Read more.
Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. However, efficient motion in such conditions requires not only mechanical flexibility but also effective path planning to ensure safety, energy efficiency, and overall task performance. Most existing path planning algorithms for snake-like robots focus primarily on finding the shortest path between the start and target positions while neglecting the optimization of energy consumption during real operations. To address this limitation, this study proposes an energy-efficient path planning method based on an improved A* algorithm enhanced with deep reinforcement learning: Dueling Double-Deep Q-Network (D3QN). An Energy Consumption Estimation Model (ECEM) is first developed to evaluate the energetic cost of snake robot motion in three-dimensional space. This model is then integrated into a new heuristic function to guide the A* search toward energy-optimal trajectories. Simulation experiments were conducted in a 3D environment to assess the performance of the proposed approach. The results demonstrate that the improved A* algorithm effectively reduces the energy consumption of the snake robot compared with conventional algorithms. Specifically, the proposed method achieves an energy consumption of 68.79 J, which is 3.39%, 27.26%, and 5.91% lower than that of the traditional A* algorithm (71.20 J), the bidirectional A* algorithm (94.61 J), and the weighted improved A* algorithm (73.11 J), respectively. These findings confirm that integrating deep reinforcement learning with an adaptive heuristic function significantly enhances both the energy efficiency and practical applicability of snake robot path planning in complex 3D environments. Full article
(This article belongs to the Special Issue Theory and Application of Bioinspired Robotics and Intelligent Control)
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19 pages, 8700 KB  
Article
Human-Inspired Force-Motion Imitation Learning with Dynamic Response for Adaptive Robotic Manipulation
by Yuchuang Tong, Haotian Liu, Tianbo Yang and Zhengtao Zhang
Biomimetics 2025, 10(12), 825; https://doi.org/10.3390/biomimetics10120825 - 9 Dec 2025
Cited by 1 | Viewed by 994
Abstract
Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper [...] Read more.
Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper presents a bioinspired imitation learning framework that models human adaptive dynamics to jointly acquire and generalize motion and force skills, enabling compliant and resilient robot behavior. The proposed framework integrates hybrid force–motion learning with dynamic response mechanisms, achieving broad skill generalization without reliance on external sensing modalities. A momentum-based force observer is combined with dynamic movement primitives (DMPs) to enable accurate force estimation and smooth motion coordination, while a broad learning system (BLS) refines the DMP forcing function through style modulation, feature augmentation, and adaptive weight tuning. In addition, an adaptive radial basis function neural network (RBFNN) controller dynamically adjusts control parameters to ensure precise, low-latency skill reproduction, and safe physical interaction. Simulations and real-world experiments confirm that the proposed framework achieves human-like adaptability, robustness, and scalability, attaining a competitive learning time of 5.56 s and a rapid generation time of 0.036 s, thereby demonstrating its efficiency and practicality for real-time applications and offering a lightweight yet powerful solution for bioinspired intelligent control in complex and unstructured environments. Full article
(This article belongs to the Special Issue Theory and Application of Bioinspired Robotics and Intelligent Control)
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