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Biomimetics
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Human-Inspired Grasp Control in Robotics 2025
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Human-Inspired Grasp Control in Robotics 2025

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

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 6603

Special Issue Editor

Dr. Yi Zhang

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: mechanism design; robotic system and technology; biomechatronics; complex system modeling and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Humans have a remarkable ability to grasp and manipulate various objects. This Special Issue, entitled “Human-Inspired Grasp Control in Robotics 2025”, tackles the challenge of enabling robots to efficiently and intuitively manipulate objects in real-world scenarios. The objective is to replicate the way humans handle objects in order to enhance the adaptability and dexterity of robotic systems. This research holds practical implications for fields such as prosthetics, manufacturing, healthcare, and household robotics, where precise and flexible object manipulation is crucial.

Traditional approaches to grasping control in robotics rely on rigid grasping models that struggle to cope with the dynamic nature of real-world environments. To address this drawback, researchers propose a human-inspired approach that draws inspiration from how humans utilize tactile sensing and feedback to adjust their grip on objects. This approach comprises key elements such as tactile sensing, object recognition, adaptive planning algorithms, and so on. It can empower the robot to adapt its grip based on the unique characteristics of an object and environmental constraints, ultimately enhancing grasp stability and success rates. It underscores the significance of emulating human grasping strategies in order to elevate the adaptability and dexterity of robotic systems. The proposed techniques hold immense potential for advancing robotic manipulation capabilities across a wide array of practical real-world tasks.

Dr. Yi Zhang
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 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

  • human-inspired grasping
  • reinforcement learning
  • tactile sensing
  • object recognition
  • force control
  • position control
  • robotic grasping
  • robot–environment interaction
  • dexterous grasping
  • grasp posture

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Related Special Issue

Published Papers (7 papers)

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Research

22 pages, 2479 KB  
Article
Adaptive Action Chunking for Robotic Imitation Learning
by Qingpeng Wen, Haomin Zhu, Yuepeng Zhang, Linzhong Xia, Bo Gao and Zhuozhen Li
Biomimetics 2026, 11(5), 316; https://doi.org/10.3390/biomimetics11050316 (registering DOI) - 2 May 2026
Abstract
Action chunking strategies in robot imitation learning struggle to dynamically balance between long-range motion efficiency and short-range operational precision due to their fixed planning horizon. This paper presents an Adaptive Action Chunking framework that enables robots to dynamically predict the optimal action chunk [...] Read more.
Action chunking strategies in robot imitation learning struggle to dynamically balance between long-range motion efficiency and short-range operational precision due to their fixed planning horizon. This paper presents an Adaptive Action Chunking framework that enables robots to dynamically predict the optimal action chunk length based on real-time visual context. We design an end-to-end dual-branch network comprising a shared visual encoder, a parallel action prediction head, and a chunk-size prediction head. Experiments on two real-world bimanual robot manipulation tasks (transport-and-place and flip-and-handover) demonstrate that the method autonomously derives two distinct intelligent strategy patterns—phase-aware switching and sustained high-frequency adjustment—in response to task uncertainty. It significantly outperforms fixed-chunk baselines in both success rate and efficiency. Ablation studies confirm that the performance gain stems from the adaptive decision-making mechanism itself. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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22 pages, 10201 KB  
Article
A Reactive Synchronized Motion Controller for Dual-Arm Cooperation with Closed-Chain Constraints
by Fengjia Ju, Zijian Wang, Mingda Ge, Hongzhe Jin and Jie Zhao
Biomimetics 2026, 11(5), 298; https://doi.org/10.3390/biomimetics11050298 - 24 Apr 2026
Viewed by 434
Abstract
When a rigid object is manipulated by dual arms to form a closed chain, the dual-arm motion must satisfy closed-chain constraints. Although synchronized motion can be achieved by strictly tracking predefined global trajectories, the presence of dynamic obstacles necessitates reactive local planning. However, [...] Read more.
When a rigid object is manipulated by dual arms to form a closed chain, the dual-arm motion must satisfy closed-chain constraints. Although synchronized motion can be achieved by strictly tracking predefined global trajectories, the presence of dynamic obstacles necessitates reactive local planning. However, existing local planning methods designed for single-arm manipulators cannot guarantee synchronization between dual arms. To address this limitation, we propose a dual-arm reactive synchronized motion controller (SMC) by incorporating closed-chain constraints on dual-arm slack velocities based on spherical geometric velocity constraints, and by implementing a flexible master-slave arm switching strategy. As a result, the proposed controller achieves synchronized dual-arm control while preserving excellent motion performance, including manipulability enhancement, obstacle avoidance, and compliance with joint angle and velocity constraints. Simulations and experiments on a humanoid upper-body robot validate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
32 pages, 21661 KB  
Article
Robust Human-to-Robot Handover System Under Adverse Lighting
by Yifei Wang, Baoguo Xu, Huijun Li and Aiguo Song
Biomimetics 2026, 11(4), 231; https://doi.org/10.3390/biomimetics11040231 - 1 Apr 2026
Viewed by 555
Abstract
Human-to-robot (H2R) handovers are critical in human–robot interaction but are challenged by complex environments that impact robot perception. Traditional RGB-based perception methods exhibit severe performance degradation under harsh lighting (e.g., glare and darkness). Furthermore, H2R handovers occur in unstructured environments populated with fine-grained [...] Read more.
Human-to-robot (H2R) handovers are critical in human–robot interaction but are challenged by complex environments that impact robot perception. Traditional RGB-based perception methods exhibit severe performance degradation under harsh lighting (e.g., glare and darkness). Furthermore, H2R handovers occur in unstructured environments populated with fine-grained visual details, such as multi-angle hand configurations and novel object geometries, where conventional semantic segmentation and grasp generation approaches struggle to generalize. To overcome lighting disturbances, we present an H2R handover system with a dual-path perception pipeline. The system fuses perception data from a stereo RGB-D camera (eye-in-hand) and a time-of-flight (ToF) camera (fixed scene) under normal lighting, and switches to the ToF camera for reliable perception under glare and darkness. In parallel, to address the complex spatial and geometric features, we augment the Point Transformer v3 (PTv3) architecture by integrating a T-Net module and a self-attention mechanism to fuse the relative positional angle features between human and robot, enabling efficient real-time 3D semantic segmentation of both the object and the human hand. For grasp generation, we extend GraspNet with a grasp selection module optimized for H2R scenarios. We validate our approach through extensive experiments: (1) a semantic segmentation dataset with 7500 annotated point clouds covering 15 objects and 5 relative angles and tested on 750 point clouds from 15 unseen objects, where our method achieves 84.4% mIoU, outperforming Swin3D-L by 3.26 percentage points with 3.2× faster inference; (2) 250 real-world handover trials comparing our method with the baseline across 5 objects, 5 hand postures, and 5 angles, showing an improvement of 18.4 percentage points in success rate; (3) 450 trials under controlled adverse lighting (darkness and glare), where our dual-path perception method achieves 82.7% overall success, surpassing single-camera baselines by up to 39.4 percentage points; and (4) a comparative experiment against a state-of-the-art multimodal H2R handover method under identical adverse lighting, where our system achieves 75.0% success (15/20) versus the baseline’s 15.0% (3/20), further confirming the lighting robustness of our design. These results demonstrate the system’s robustness and generalization in challenging H2R handover scenarios. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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18 pages, 3476 KB  
Article
An Optimization Method for an Active Multi-Unit Prosthetic Socket with Dynamic Adaptability in Multi-Task Scenarios
by Yawen Hu, Li Jiang, Chunying Zou, Bangchu Yang, Tianquan Han and Ming Cheng
Biomimetics 2026, 11(2), 129; https://doi.org/10.3390/biomimetics11020129 - 11 Feb 2026
Viewed by 605
Abstract
As a core functional component of the prosthetic system, the prosthetic socket’s adaptability to the residual limb is directly correlated with the prosthetic’s performance, comfort level, and safety profile. Although traditional sockets can satisfy basic suspension requirements, they commonly suffer from inherent drawbacks [...] Read more.
As a core functional component of the prosthetic system, the prosthetic socket’s adaptability to the residual limb is directly correlated with the prosthetic’s performance, comfort level, and safety profile. Although traditional sockets can satisfy basic suspension requirements, they commonly suffer from inherent drawbacks in practical applications, including uneven pressure distribution, poor air permeability, and inadequate adaptability to the morphological variations of individual residual limbs. To enhance socket adaptability across multi-task scenarios, this study proposes an intelligent physiological adaptation-based optimal design method for active upper-limb prosthetic sockets. Specifically, this method first employs a dynamic force optimization algorithm for multi-contact units oriented to prosthetic manipulation tasks, which real-timely optimizes the output force of each unit under varying external loads to achieve stable socket suspension with minimal interface pressure. Second, biomechanical experiments are conducted to obtain the pain threshold distribution characteristics of forearm soft tissues under compressive loads, thereby providing a physiological basis for the spatial layout of the contact units. Furthermore, the mechanical performance of different socket structures is evaluated under various representative task scenarios, with peak normal force, mean normal force, and force distribution variance adopted as the key comfort evaluation indices. The results demonstrate that the proposed active multi-unit socket, particularly the double-layered eight-unit symmetric radial staggered configuration, enables a robust balance between comfort and stability across diverse task scenarios, thereby establishing an effective and scalable design paradigm for long-term adaptive upper-limb prosthetic sockets. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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30 pages, 1992 KB  
Article
Biomimetic Approach to Designing Trust-Based Robot-to-Human Object Handover in a Collaborative Assembly Task
by S. M. Mizanoor Rahman
Biomimetics 2026, 11(1), 14; https://doi.org/10.3390/biomimetics11010014 - 27 Dec 2025
Viewed by 1027
Abstract
We presented a biomimetic approach to designing robot-to-human handover of objects in a collaborative assembly task. We developed a human–robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operations of a product in a flexible [...] Read more.
We presented a biomimetic approach to designing robot-to-human handover of objects in a collaborative assembly task. We developed a human–robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operations of a product in a flexible manufacturing setup. Firstly, we investigated human psychology and biomechanics (kinetics and kinematics) for human-to-robot handover of an object in the human–robot collaborative set-up in three separate experimental conditions: (i) human possessed high trust in the robot, (ii) human possessed moderate trust in the robot, and (iii) human possessed low trust in the robot. The results showed that human psychology was significantly impacted by human trust in the robot, which also impacted the biomechanics of human-to-robot handover, i.e., human hand movement slowed down, the angle between human hand and robot arm increased (formed a braced handover configuration), and human grip forces increased if human trust in the robot decreased, and vice versa. Secondly, being inspired by those empirical results related to human psychology and biomechanics, we proposed a novel robot-to-human object handover mechanism (strategy). According to the novel handover mechanism, the robot varied its handover configurations and motions through kinematic redundancy with the aim of reducing potential impulse forces on the human body through the object during the handover when robot trust in the human was low. We implemented the proposed robot-to-human handover mechanism in the human–robot collaborative assembly task in the hybrid cell. The experimental evaluation results showed significant improvements in human–robot interaction (HRI) in terms of transparency, naturalness, engagement, cooperation, cognitive workload, and human trust in the robot, and in overall performance in terms of handover safety, handover success rate, and assembly efficiency. The results can help design and develop human–robot handover mechanisms for human–robot collaborative tasks in various applications such as industrial manufacturing and manipulation, medical surgery, warehouse, transport, logistics, construction, machine shops, goods delivery, etc. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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11 pages, 2963 KB  
Communication
Optimization Design of Haptic Units for Perception Feedback Interfaces Based on Vibrotactile Amplitude Modulation
by Weichao Guo, Jingchen Huang, Lechuan Zhou, Yun Fang, Li Jiang and Xinjun Sheng
Biomimetics 2025, 10(9), 597; https://doi.org/10.3390/biomimetics10090597 - 7 Sep 2025
Viewed by 1161
Abstract
Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing [...] Read more.
Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing between vibration units is a significant challenge in the design of haptic interfaces. This work focuses on the joint optimization design of vibration source characteristics and packaging materials of vibration units. From a theoretical modeling perspective, we explore the correlation between material properties and the amplitude of vibrations generated on the skin surface. A three-layer vibration unit optimization design scheme using a pogo pin structure is thus proposed. Parameters are optimized through finite element analysis, and experimental results prove that the three-layer vibration unit with pogo pins has amplitude modulation capabilities, laying the foundation for the design of array-based vibration tactile feedback interfaces and human-inspired grasp control. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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19 pages, 11348 KB  
Article
Vision-Based Grasping Method for Prosthetic Hands via Geometry and Symmetry Axis Recognition
by Yi Zhang, Yanwei Xie, Qian Zhao, Xiaolei Xu, Hua Deng and Nianen Yi
Biomimetics 2025, 10(4), 242; https://doi.org/10.3390/biomimetics10040242 - 15 Apr 2025
Cited by 1 | Viewed by 1970
Abstract
This paper proposes a grasping method for prosthetic hands based on object geometry and symmetry axis. The method utilizes computer vision to extract the geometric shape, spatial position, and symmetry axis of target objects and selects appropriate grasping modes and postures through the [...] Read more.
This paper proposes a grasping method for prosthetic hands based on object geometry and symmetry axis. The method utilizes computer vision to extract the geometric shape, spatial position, and symmetry axis of target objects and selects appropriate grasping modes and postures through the extracted features. First, grasping patterns are classified based on the analysis of hand-grasping movements. A mapping relationship between object geometry and grasp patterns is established. Then, target object images are captured using binocular depth cameras, and the YOLO algorithm is employed for object detection. The SIFT algorithm is applied to extract the object’s symmetry axis, thereby determining the optimal grasp point and initial hand posture. An experimental platform is built based on a seven-degree-of-freedom (7-DoF) robotic arm and a multi-mode prosthetic hand to conduct grasping experiments on objects with different characteristics. Experimental results demonstrate that the proposed method achieves high accuracy and real-time performance in recognizing object geometric features. The system can automatically match appropriate grasp modes according to object features, improving grasp stability and success rate. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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