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Electronics
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Advancements in Robotics: Perception, Manipulation, and Interaction
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Advancements in Robotics: Perception, Manipulation, and Interaction

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 7968

Special Issue Editors

Dr. Peng Zhou

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Guest Editor
Computer Science Department, The University of Hong Kong, Hong Kong, China
Interests: robot manipulation; robot learning; task and motion planning
Special Issues, Collections and Topics in MDPI journals
Dr. Anqing Duan

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Guest Editor
Robotics Department, Mohamed Bin Zayed University of Artificial Intelligence, Masdar, United Arab Emirates
Interests: learning by demonstration; reinforcement learning; rehabilitation robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Liang Lu

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Guest Editor
Computer Science Department, The University of Hong Kong, Hong Kong 999077, China
Interests: active perception; scene understanding; field robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Jiajun Xu

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Guest Editor
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: wearable robotics; biomimetic robotics; medical robotics; human–robot interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Wanyu Ma

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Guest Editor
Faculty of Engineering, The Chinese University of Hong Kong, The Chinese University of Hong Kong, Hong Kong 999077, China
Interests: robot manipulation; machine intelligence; human-robot interaction
Dr. David Navarro-Alarcon

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Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: robotics; control systems

Special Issue Information

Dear Colleagues,

The realm of robotics has witnessed profound advancements, expanding its reach into numerous fields where the synergy between human and machine is paramount. Innovations in robotic perception, manipulation, and interaction have not only propelled the capabilities of robots but have also revolutionized the way they collaborate with humans in diverse environments. To capture and disseminate the latest breakthroughs in this dynamic domain, we are organizing a Special Issue titled "Advancements in Robotics: Perception, Manipulation, and Interaction" with Electronics.

This Special Issue aims to explore the cutting-edge developments in robotic systems that enhance sensory perception, refine manipulation skills, and enrich interactive experiences. We are calling for contributions that shed light on the theoretical underpinnings, practical applications, and transformative impacts of these advancements. We welcome submissions that delve into the implementation, deployment, and real-world outcomes of novel robotic technologies.

Authors are encouraged to submit original research that encapsulates a broad spectrum of topics within the scope of robotic advancements. The areas of interest for this Special Issue include, but are not limited to, the following:      

  • Sensor technologies and perception algorithms for robotics;
  • Robotic manipulation in unstructured or dynamic environments;
  • Human–robot interaction and collaboration;
  • Machine learning and AI applications in robotic systems;
  • Autonomous decision making and control in robotics;
  • Robotic assistance in healthcare, manufacturing, and service sectors;
  • Ethical considerations and social impacts of robotic interactions;
  • Integration of robotics with IoT and smart infrastructure;
  • Advances in robotic mobility and dexterity;
  • Haptic feedback and teleoperation in robotic systems;
  • Robotics in extreme or hazardous environments;
  • Personalized and adaptive robotic assistance.

We are looking forward to your valuable contributions to this Special Issue, which we believe will serve as a pivotal platform for academics, researchers, industrial experts, and practitioners to exchange insights, foster collaborations, and advance the state of the art in robotics.

Dr. Peng Zhou
Dr. Anqing Duan
Dr. Liang Lu
Dr. Jiajun Xu
Dr. Wanyu Ma
Dr. David Navarro-Alarcon
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • robotics and AI robot perception
  • active perception
  • multi-modal perception
  • scene understanding
  • field robotics
  • robot grasping
  • robot manipulation
  • robot learning
  • task and motion planning
  • rehabilitation robotics
  • biomimetic robotics
  • human–robot interaction control

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

Published Papers (7 papers)

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Research

21 pages, 3136 KiB  
Article
Negative Expressions by Social Robots and Their Effects on Persuasive Behaviors
by Chinenye Augustine Ajibo, Carlos Toshinori Ishi and Hiroshi Ishiguro
Electronics 2025, 14(13), 2667; https://doi.org/10.3390/electronics14132667 - 1 Jul 2025
Viewed by 411
Abstract
The ability to effectively engineer robots with appropriate social behaviors that conform to acceptable social norms and with the potential to influence human behavior remains a challenging area in robotics. Given this, we sought to provide insights into “what can be considered a [...] Read more.
The ability to effectively engineer robots with appropriate social behaviors that conform to acceptable social norms and with the potential to influence human behavior remains a challenging area in robotics. Given this, we sought to provide insights into “what can be considered a socially appropriate and effective behavior for robots charged with enforcing social compliance of various magnitudes”. To this end, we investigate how social robots can be equipped with context-inspired persuasive behaviors for human–robot interaction. For this, we conducted three separate studies. In the first, we explored how the android robot “ERICA” can be furnished with negative persuasive behaviors using a video-based within-subjects design with N = 50 participants. Through a video-based experiment employing a mixed-subjects design with N = 98 participants, we investigated how the context of norm violation and individual user traits affected perceptions of the robot’s persuasive behaviors in the second study. Lastly, we investigated the effect of the robot’s appearance on the perception of its persuasive behaviors, considering two humanoids (ERICA and CommU) through a within-subjects design with N = 100 participants. Findings from these studies generally revealed that the robot could be equipped with appropriate and effective context-sensitive persuasive behaviors for human–robot interaction. Specifically, the more assertive behaviors (displeasure and anger) of the agent were found to be effective (p < 0.01) as a response to a situation of repeated violation after an initial positive persuasion. Additionally, the appropriateness of these behaviors was found to be influenced by the severity of the violation. Specifically, negative behaviors were preferred for persuasion in situations where the violation affects other people (p < 0.01), as in the COVID-19 adherence and smoking prohibition scenarios. Our results also revealed that the preference for the negative behaviors of the robots varied with users’ traits, specifically compliance awareness (CA), agreeableness (AG), and the robot’s embodiment. The current findings provide insights into how social agents can be equipped with appropriate and effective context-aware persuasive behaviors. It also suggests the relevance of a cognitive-based approach in designing social agents, particularly those deployed in sensitive social contexts. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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24 pages, 2653 KiB  
Article
DARC: Disturbance-Aware Redundant Control for Human–Robot Co-Transportation
by Al Jaber Mahmud, Amir Hossain Raj, Duc M. Nguyen, Xuesu Xiao and Xuan Wang
Electronics 2025, 14(12), 2480; https://doi.org/10.3390/electronics14122480 - 18 Jun 2025
Viewed by 279
Abstract
This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and a [...] Read more.
This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and a manipulator arm, compensates for uncertain human behaviors and internal actuation noise through a two-step iterative process. At each planning horizon, a candidate set of feasible joint configurations is generated using a Conditional Variational Autoencoder (CVAE). From this set, one configuration is selected by minimizing an estimated control cost computed via a disturbance-aware Discrete Algebraic Riccati Equation (DARE), which also provides the optimal control inputs for both the mobile base and the manipulator arm. We derive the disturbance-aware DARE and validate DARC with simulated experiments with a Fetch robot. Evaluations across various trajectories and disturbance levels demonstrate that our proposed DARC framework outperforms baseline algorithms that lack disturbance modeling, pose optimization, or both. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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27 pages, 9977 KiB  
Article
Mergeable Probabilistic Voxel Mapping for LiDAR–Inertial–Visual Odometry
by Balong Wang, Nassim Bessaad, Huiying Xu, Xinzhong Zhu and Hongbo Li
Electronics 2025, 14(11), 2142; https://doi.org/10.3390/electronics14112142 - 24 May 2025
Viewed by 664
Abstract
To address the limitations of existing LiDAR–visual fusion methods in adequately accounting for map uncertainties induced by LiDAR measurement noise, this paper introduces a LiDAR–inertial–visual odometry framework leveraging mergeable probabilistic voxel mapping. The method innovatively employs probabilistic voxel models to characterize uncertainties in [...] Read more.
To address the limitations of existing LiDAR–visual fusion methods in adequately accounting for map uncertainties induced by LiDAR measurement noise, this paper introduces a LiDAR–inertial–visual odometry framework leveraging mergeable probabilistic voxel mapping. The method innovatively employs probabilistic voxel models to characterize uncertainties in environmental geometric plane features and optimizes computational efficiency through a voxel merging strategy. Additionally, it integrates color information from cameras to further enhance localization accuracy. Specifically, in the LiDAR–inertial odometry (LIO) subsystem, a probabilistic voxel plane model is constructed for LiDAR point clouds to explicitly represent measurement noise uncertainty, thereby improving the accuracy and robustness of point cloud registration. A voxel merging strategy based on the union-find algorithm is introduced to merge coplanar voxel planes, reducing computational load. In the visual–inertial odometry (VIO) subsystem, image tracking points are generated through a global map projection, and outlier points are eliminated using a random sample consensus algorithm based on a dynamic Bayesian network. Finally, state estimation accuracy is enhanced by jointly optimizing frame-to-frame reprojection errors and frame-to-map RGB color errors. Experimental results demonstrate that the proposed method achieves root mean square errors (RMSEs) of absolute trajectory error at 0.478 m and 0.185 m on the M2DGR and NTU-VIRAL datasets, respectively, while attaining real-time performance with an average processing time of 39.19 ms per-frame on the NTU-VIRAL datasets. Compared to state-of-the-art approaches, our method exhibits significant improvements in both accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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25 pages, 6180 KiB  
Article
Recursive PID-NT Estimation-Based Second-Order SMC Strategy for Knee Exoskeleton Robots: A Focus on Uncertainty Mitigation
by Vahid Behnamgol, Mohamad Asadi, Sumeet S. Aphale and Behnaz Sohani
Electronics 2025, 14(7), 1455; https://doi.org/10.3390/electronics14071455 - 3 Apr 2025
Cited by 2 | Viewed by 440
Abstract
This study introduces a modified second-order super-twisting sliding mode control algorithm designed to enhance the precision and robustness of knee exoskeleton robots by incorporating advanced uncertainty mitigation techniques. The key contribution of this research is the development of an efficient estimation mechanism capable [...] Read more.
This study introduces a modified second-order super-twisting sliding mode control algorithm designed to enhance the precision and robustness of knee exoskeleton robots by incorporating advanced uncertainty mitigation techniques. The key contribution of this research is the development of an efficient estimation mechanism capable of accurately identifying model parameter uncertainties and patients’ unwanted action torques disturbance within a finite time horizon, thereby improving overall system performance. The proposed control framework ensures smooth and precise control signal dynamics while effectively suppressing chattering effects, a common drawback in conventional sliding mode control methodologies. The theoretical foundation of the algorithm is rigorously established through the formulation of a PID non-singular terminal sliding variable, which ensures finite time stability in the sliding phase and a comprehensive Lyapunov-based stability analysis assuming that the upper bound of the uncertainty and its derivative are known in the reaching phase, which collectively guarantee the system’s robustness and reliability. Through simulations, the efficacy of the proposed control system is evaluated in its ability to track diverse desired knee angles, demonstrate robustness against disturbances, such as those caused by the patient’s foot reaction, and handle a 20% uncertainty in the model parameters. Additionally, the system’s effectiveness is assessed by three individuals with varying parameters. Notably, the controller gains remain consistent across all scenarios. This research constitutes a significant advancement in the domain of knee exoskeleton control, offering a more reliable and precise methodology for addressing model uncertainties. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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20 pages, 21190 KiB  
Article
Whole-Body Control with Uneven Terrain Adaptability Strategy for Wheeled-Bipedal Robots
by Biao Wang, Yaxian Xin, Chao Chen, Zihao Song, Baoshuai Sun and Tianshuai Guo
Electronics 2025, 14(1), 198; https://doi.org/10.3390/electronics14010198 - 5 Jan 2025
Viewed by 1532
Abstract
Wheeled-bipedal robots (WBRs) integrate the locomotion efficiency and terrain adaptability of legged and wheeled robots. However, terrain adaptability is significantly influenced by the control system. This paper proposes a hierarchical control method for WBRs that includes an active force solver, a whole-body pose [...] Read more.
Wheeled-bipedal robots (WBRs) integrate the locomotion efficiency and terrain adaptability of legged and wheeled robots. However, terrain adaptability is significantly influenced by the control system. This paper proposes a hierarchical control method for WBRs that includes an active force solver, a whole-body pose planner and a whole-body torque controller. The active force solver based on model predictive control (MPC) was constructed to calculate the active force from the wheeled legs to the torso to achieve the torso’s desired motion tasks. The whole-body pose planner based on the terrain adaptability strategy provides whole-body joint trajectories that can achieve dynamic balance and movement simultaneously without external sensing information. The whole-body torque controller is used to calculate whole-body joint torque based on the active force reference and joint motion reference. Finally, two simulation experiments were conducted to verify the effectiveness of the proposed method on uneven terrain. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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31 pages, 12314 KiB  
Article
Utilizing Attention-Enhanced Deep Neural Networks for Large-Scale Preliminary Diabetes Screening in Population Health Data
by Hongwei Hu, Wenbo Dong, Jianming Yu, Shiyan Guan and Xiaofei Zhu
Electronics 2024, 13(21), 4177; https://doi.org/10.3390/electronics13214177 - 24 Oct 2024
Viewed by 1652
Abstract
Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an [...] Read more.
Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an Attention-based Feature Weighting Layer combined with deep neural network layers to achieve precise diabetes prediction. In this study, we utilized the Diabetes-NHANES dataset and the Pima Indians Diabetes dataset. To handle significant missing values and outliers, group median imputation was applied. Oversampling techniques were used to balance the diabetes and non-diabetes groups. The data were processed through an Attention-based Feature Weighting Layer for feature extraction, producing a feature matrix. This matrix was subjected to Hadamard product operations with the raw data to obtain weighted data, which were subsequently input into deep neural network layers for training. The parameters were fine-tuned and the L2 regularization and dropout layers were added to enhance the generalization performance of the model. The model’s reliability was thoroughly assessed through various metrics, including the accuracy, precision, recall, F1 score, mean squared error (MSE), and R2 score, as well as the ROC and AUC curves. The proposed model achieved a prediction accuracy of 98.4% in the Pima Indians Diabetes dataset. When the test dataset was expanded to the large-scale Diabetes-NHANES dataset, which contains 52,390 samples, the test precision of the model improved further to 99.82%, with an AUC of 0.9995. A comparative analysis was conducted using multiple models, including logistic regression with L1 regularization, support vector machine (SVM), random forest, K-nearest neighbors (KNNs), AdaBoost, XGBoost, and the latest semi-supervised XGBoost. The feature extraction method using attention mechanisms was compared with the classical feature selection methods, Lasso and Ridge. The experiments were performed on the same dataset, and the conclusion was that the Attention-based Ensemble Deep Neural Network (AEDNN) outperformed all the aforementioned methods. These results indicate that the model not only performs well on smaller datasets but also fully leverages its advantages on larger datasets, demonstrating strong generalization ability and robustness. The proposed model can effectively assist clinicians in the early screening of diabetes patients. This is particularly beneficial for the preliminary screening of high-risk individuals in large-scale, extensive healthcare datasets, followed by detailed examination and diagnosis. Compared to the existing methods, our AEDNN model showed an overall performance improvement of 1.75%. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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19 pages, 4224 KiB  
Article
A Rigid–Flexible Supernumerary Robotic Arm/Leg: Design, Modeling, and Control
by Jiajun Xu, Mengcheng Zhao, Tianyi Zhang and Aihong Ji
Electronics 2024, 13(20), 4106; https://doi.org/10.3390/electronics13204106 - 18 Oct 2024
Cited by 3 | Viewed by 1766
Abstract
As humans’ additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed [...] Read more.
As humans’ additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed to develop SRLs simultaneously realizing flexible operation and stable support. In this paper, an SRL with FRAs was designed and implemented. The analytic model of the FRA was established to formulate the movement trajectory and stiffness profile of the SRL. A hierarchical hidden Markov model (HHMM) was proposed to recognize the wearer’s motion intention and control the SRL to complete the specific working mode and motion type. Experiments were conducted to exhibit the feasibility and superiority of the proposed robot. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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