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Design and Implementation of a Gesture-Controlled Robotic Platform for Applied Education in Human–Robot Interaction
Numerical and Experimental Validation of an Autonomous Navigation and Mapping Framework for Mobile Robotics
Pose-Constrained Path Optimization for Manipulators with Contact Feedback from Sparse Waypoints
A Hybrid Master–Slave Fuzzy Cascade Control Strategy for Two-Wheeled Self-Balancing Robot with Wheel Synchronization

Journal Description

Robotics

Robotics is an international, peer-reviewed, open access journal on robotic systems in theory, design, and applications, published monthly online by MDPI. The International Federation for the Promotion of Mechanism and Machine Science (IFToMM) and Robotic Global Surgical Society (TROGSS) are affiliated with Robotics and its members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Robotics) / CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.

Impact Factor: 3.6 (2025); 5-Year Impact Factor: 4.0 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2218-6581

Latest Articles

42 pages, 11702 KB

Open AccessReview

The Evolution of Image Segmentation from Classical Techniques to Deep Learning: A Survey

by Moteaal Asadi Shirzi and Mehrdad R. Kermani

Robotics 2026, 15(9), 169; https://doi.org/10.3390/robotics15090169 - 3 Sep 2026

Image segmentation is a fundamental step in computer vision and a cornerstone of robotic perception, serving as the foundation for interpreting data acquired from vision sensors, enabling robots to analyze complex visual environments, identify and localize objects, and support intelligent decision-making and autonomous [...] Read more.

Image segmentation is a fundamental step in computer vision and a cornerstone of robotic perception, serving as the foundation for interpreting data acquired from vision sensors, enabling robots to analyze complex visual environments, identify and localize objects, and support intelligent decision-making and autonomous control. It plays a critical role in applications such as autonomous navigation, robotic manipulation, medical robotics, agricultural robotics, autonomous vehicles, and human–robot interaction. Image segmentation has evolved from classical methods, which relied on handcrafted rules and mathematical models, to deep learning approaches that learn complex visual patterns directly from data. This evolution reflects advances in algorithms, computational power, and the theoretical foundations of mathematics and data science. Modern deep learning methods rely heavily on large, well-annotated datasets to train sophisticated neural networks. Yet, classical techniques remain valuable in certain scenarios, offering faster, reliable results without extensive computational requirements. Understanding the strengths and limitations of both approaches is key to selecting the right method. This paper surveys image segmentation techniques, comparing them in terms of accuracy, computational cost, and processing speed to guide informed method selection. Full article

(This article belongs to the Special Issue Artificial Vision Systems for Robotics)

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Graphical abstract

17 pages, 8888 KB

Open AccessArticle

Kinematic and Static Force Analysis of the ABB IRB 360 Delta Parallel Robot

by Dimitar Chakarov

Robotics 2026, 15(9), 168; https://doi.org/10.3390/robotics15090168 - 28 Aug 2026

Parallel Delta robots are widely used in high-speed industrial automation, particularly in food-processing operations that require short cycle times and precise manipulation. Their performance under complex loading conditions, however, remains critical for reliable integration into mechatronic production systems. In many technological processes, the [...] Read more.

Parallel Delta robots are widely used in high-speed industrial automation, particularly in food-processing operations that require short cycle times and precise manipulation. Their performance under complex loading conditions, however, remains critical for reliable integration into mechatronic production systems. In many technological processes, the end effector experiences not only gravitational forces but also additional loads caused by product contact, lateral disturbances, and dynamic gripping actions. These conditions influence actuator torque demand, structural behaviour, and workspace feasibility. To address this, the present study develops a complete kinematic formulation and a force analysis model for the ABB IRB 360 3/1130 FlexPicker^® Delta robot sourced from ABB Robotics, Västerås, Sweden. The models enable computer-based simulation of the robot’s behaviour under both vertical and horizontally oriented external forces. The results show how combined loading affects torque distribution and may restrict feasible workspace regions. These findings support motion planning, task feasibility assessment, and the reliable integration of high-speed parallel manipulators in industrial mechatronic environments. Full article

(This article belongs to the Special Issue Integrating Robotics into High-Accuracy Industrial Operations, 2nd Edition)

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17 pages, 1366 KB

Open AccessArticle

Hybrid Zero-Shot Interactive Navigation with LLMs: Path Planning Under Dual Constraints of Speech and Environment

by Fan Yang, Jing Wu, Timur Kuzu, Hendrik Benz and Katharina Klemt-Albert

Robotics 2026, 15(9), 167; https://doi.org/10.3390/robotics15090167 - 28 Aug 2026

This study proposes a novel hybrid interactive navigation framework for mobile robots, designed to enable robots to operate under dual constraints imposed by both the environment and the speech of accompanying humans in challenging future collaborative working scenarios. By leveraging human perceptual capabilities, [...] Read more.

This study proposes a novel hybrid interactive navigation framework for mobile robots, designed to enable robots to operate under dual constraints imposed by both the environment and the speech of accompanying humans in challenging future collaborative working scenarios. By leveraging human perceptual capabilities, the proposed framework significantly enhances the obstacle-avoidance capabilities and flexibility of robots. Specifically, an innovative composition algorithm is introduced to integrate traditional costmap-based navigation with a newly proposed LLM-assisted voice-based interaction method, thereby achieving real-time human–robot collaborative navigation with complementary advantages. Within this framework, robots can not only rely on spatial sensors to avoid obstacles but also follow verbal instructions from humans to bypass hazards that are difficult to detect. Moreover, the volume of speech is innovatively incorporated as a fusion weight, allowing the accompanying human to naturally guide the robot through voice volume modulation. To validate the feasibility and performance of the proposed framework and algorithm, we conducted both simulation and real-world experiments. A series of ablation and comparative studies was conducted to evaluate the merits and limitations of various configurations, ultimately providing optimal configurations based on the results. This work expands the scope of real-time human–robot interaction in navigation, offering new perspectives for future research. Full article

(This article belongs to the Special Issue SLAM and Adaptive Navigation for Robotics)

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34 pages, 10708 KB

Open AccessArticle

A Hybrid Verification Method for an Actuated Lower-Limb Exoskeleton Based on Mathematical and Simulation Analyses

by Artem Obukhov, Nikita Mayorov, Daniil Teselkin, Denis Dedov and Maxim Shiltsyn

Robotics 2026, 15(9), 166; https://doi.org/10.3390/robotics15090166 - 27 Aug 2026

This study proposes a hybrid method for pre-design verification of the geometric and biomechanical compatibility of experimentally recorded gait trajectories with a lower-limb exoskeleton. The method integrates joint-space kinematic analysis, CAD-based self-collision detection in a Unity digital twin, anatomical hip and knee constraints, [...] Read more.

This study proposes a hybrid method for pre-design verification of the geometric and biomechanical compatibility of experimentally recorded gait trajectories with a lower-limb exoskeleton. The method integrates joint-space kinematic analysis, CAD-based self-collision detection in a Unity digital twin, anatomical hip and knee constraints, and experimentally recorded gait trajectories. Gait data were obtained from 30 healthy participants at 12 treadmill speeds ranging from 0.5 to 6.0 km/h using multicamera markerless motion capture. Of 54,481 evaluated configurations, 21,450 remained after self-collision detection and 4080 after anatomical filtering, corresponding to 7.49% of the initial discretized domain. The mean proportion of experimental trajectory points excluded because of mechanical self-collisions increased from 2.70% at 0.5 km/h to 30.49% at 6.0 km/h. Walking speed had a significant and large overall effect (

χ_{F}^{2} (11) = 242.38

,

p < 0.001

, Kendall’s

W = 0.734

), and the median participant-specific Spearman coefficient was

ρ = 0.920

. The proposed framework provides a quantitative pre-screening tool for identifying critical gait configurations before powered prototype testing and can support mechanical redesign and definition of controller constraints. Full article

(This article belongs to the Section Medical Robotics and Service Robotics)

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Graphical abstract

33 pages, 26845 KB

Open AccessArticle

Robust Adaptive Koopman MPC Under Structured and Stochastic Uncertainty for Soft Continuum Robots

by Ali Ashraf, Ayman A. Nada, Hiroyuki Ishii and Haitham El-Hussieny

Robotics 2026, 15(9), 165; https://doi.org/10.3390/robotics15090165 - 27 Aug 2026

Soft continuum robots exhibit highly nonlinear and configuration-dependent dynamics, making accurate trajectory tracking challenging under model uncertainty and external disturbances. This paper presents an adaptive Koopman-based model predictive control (MPC) framework for a tendon-driven soft continuum robot and evaluates its performance through comprehensive [...] Read more.

Soft continuum robots exhibit highly nonlinear and configuration-dependent dynamics, making accurate trajectory tracking challenging under model uncertainty and external disturbances. This paper presents an adaptive Koopman-based model predictive control (MPC) framework for a tendon-driven soft continuum robot and evaluates its performance through comprehensive closed-loop simulations. A lifted linear Koopman model is identified from experimentally collected robot data and incorporated into an MPC formulation to provide computationally efficient prediction while capturing dominant nonlinear behavior. To compensate for plant–model mismatch and time-varying uncertainties, an online Recursive Least Squares (RLS) adaptation mechanism is integrated into the Koopman–MPC framework, enabling continuous model refinement during closed-loop operation without repeated offline retraining. The proposed controller is evaluated through comprehensive closed-loop simulations on circular, triangular, helical, and figure-eight trajectories under stochastic disturbances and structured parametric bias conditions using a Koopman model identified from experimentally collected robot data. Results demonstrate consistent improvements in tracking performance compared with fixed Koopman MPC while maintaining real-time computational feasibility. Under structured parametric bias, the proposed controller reduces the mean tracking error from 9.30 mm to 1.36 mm during circular trajectory tracking and achieves sub-millimeter accuracy in several operating conditions. These findings highlight the potential of online Koopman model adaptation for predictive control of soft continuum robots operating under uncertainty. Full article

(This article belongs to the Special Issue Soft Robotic Actuation and Locomotion: The State of the Art)

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16 pages, 2920 KB

Open AccessArticle

Beyond Odometry Accuracy: How Encoder Resolution and IMU Fusion Affect Particle Filter Localization in Differential-Drive Robots

by Lim Yun Ler, Thangavel Bhuvaneswari, Min Thu Soe and Muhammad bin Hishamuddin

Robotics 2026, 15(9), 164; https://doi.org/10.3390/robotics15090164 - 25 Aug 2026

Encoder resolution and sensor fusion architecture directly influence odometric drift in autonomous mobile robots (AMRs), yet their interaction with particle filter localization remains insufficiently characterized. This study evaluates four encoder resolutions (90, 512, 1024, and 4096 PPR) in wheel-odometry-only (System A) and EKF-based [...] Read more.

Encoder resolution and sensor fusion architecture directly influence odometric drift in autonomous mobile robots (AMRs), yet their interaction with particle filter localization remains insufficiently characterized. This study evaluates four encoder resolutions (90, 512, 1024, and 4096 PPR) in wheel-odometry-only (System A) and EKF-based IMU-fused (System B) configurations across multiple navigation scenarios using ROS AMCL localization. Increasing resolution significantly reduced drift, with 512 PPR achieving most of the improvement observed at 4096 PPR and diminishing returns beyond 1024 PPR. IMU fusion consistently improved odometry accuracy and reduced variance, although its benefit depended on trajectory complexity rather than encoder resolution alone. However, improved odometry did not uniformly translate into improved localization. In several cases, the IMU-fused system exhibited reduced AMCL stability despite lower odometric drift. These results demonstrate that encoder selection and sensor fusion must be considered alongside particle filter parameterization, highlighting the importance of estimator compatibility in ROS-based AMR localization architectures. Full article

(This article belongs to the Section Industrial Robots and Automation)

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27 pages, 31292 KB

Open AccessArticle

Constrained Close Loop Model Predictive Control Architecture for Deformable Linear Objects for Dynamic Motions

by Marc Kilian Klankers and Jochen J. Steil

Robotics 2026, 15(9), 163; https://doi.org/10.3390/robotics15090163 - 24 Aug 2026

Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a generic spatial representation and the difficulty of real-time state estimation hinder effective closed-loop manipulation. Building upon our previously established state estimation framework and [...] Read more.

Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a generic spatial representation and the difficulty of real-time state estimation hinder effective closed-loop manipulation. Building upon our previously established state estimation framework and control architecture, this paper introduces a constrained, closed-loop model predictive control (MPC) approach for highly dynamic DLO manipulation. We integrate hardware and environmental limits into the MPC as soft constraints, enabling tasks like dynamic tracking with endpoint wall avoidance. Additionally, we demonstrate challenging maneuvers, such as swinging a DLO through a narrow slot in open-loop. Furthermore, we enhance our prior state estimation method by incorporating mid-object via points, significantly improving shape reconstruction and enabling the prediction of non-monotonically curved geometries. Finally, we present an extensive evaluation of the complete control architecture using two simulated and five physical DLOs. This analysis assesses the critical influence of kinematic segment discretization, object dynamics, and distribution shifts on overall tracking performance, thereby validating the efficacy and robustness of the approach. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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26 pages, 4207 KB

Open AccessArticle

A Novel Compact Rolling Element Eccentric Planetary Gearbox Design for Lightweight and Backdrivable Wearable Robots Actuators

by Riccardo Bezzini, Simon Fritsch, Giulia Bassani, Carlo Alberto Avizzano and Alessandro Filippeschi

Robotics 2026, 15(9), 162; https://doi.org/10.3390/robotics15090162 - 22 Aug 2026

Wearable assistive exoskeletons require lightweight, compact, and backdrivable transmission systems with low output impedance to ensure safe and comfortable human–robot interaction. These efficient, modular actuators benefit from reduction mechanisms that minimize axial bulk while providing high motion regularity. While existing transmissions perform well [...] Read more.

Wearable assistive exoskeletons require lightweight, compact, and backdrivable transmission systems with low output impedance to ensure safe and comfortable human–robot interaction. These efficient, modular actuators benefit from reduction mechanisms that minimize axial bulk while providing high motion regularity. While existing transmissions perform well on some of these metrics, their practical implementation is often constrained by geometric complexity, low backdrivability, limited reduction ratios, or standard component sizes. This paper presents a novel combination of a Rolling Element Eccentric (REE) stage and a planetary gearbox, specifically designed for wearable exoskeleton actuation. The proposed architecture integrates a bearing-based REE drive concentrically within the sun gear of a planetary transmission, reducing mechanical complexity and friction and improving regularity. Moreover, the design exploits additively manufactured bearings, enabling substantial weight reduction, reduced encumbrance, and increased design freedom without reliance on standard bearing dimensions. A prototype reducer has been designed and fabricated using additive manufacturing techniques. It was experimentally evaluated and compared with state-of-the-art transmission designs. These investigations demonstrated low friction, minimal backlash, good torsional stiffness, and sufficient backdrivability, despite the high reduction ratio, while maintaining a compact, flat form factor. The experimental results indicate that the proposed rolling element eccentric planetary transmission is a viable and effective solution for lightweight, efficient, axially compact (independently of the implemented reduction ratio), and backdrivable actuators in assistive wearable robotics. Full article

(This article belongs to the Special Issue Nature-Inspired Intelligent Robotics: Soft Actuators, Magnetic Control, and AI for Biomedical Innovation)

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35 pages, 11000 KB

Open AccessArticle

Experimental Evaluation of an RHex-Inspired Hexapod Robot Under Varying Terrain Roughness, Compliance, and Leg Thickness

by Jared Jan Abayan, Ethan Brook Ong, Rudiant Crystoffer Crisostomo, Brent Ambross Mariñas, John Carlo Imbao, Andrei Miguel Enriquez, Rovick Tarife, Ronnie Concepcion II and Argel Bandala

Robotics 2026, 15(8), 161; https://doi.org/10.3390/robotics15080161 - 19 Aug 2026

This study presents the design, embedded implementation, and screening-level experimental terrain-performance evaluation of an RHex-inspired hexapod robot using a fixed encoder-assisted alternating-tripod state-machine gait. The work aims to provide an experimentally grounded assessment of how terrain properties and practical leg-thickness variation influence the [...] Read more.

This study presents the design, embedded implementation, and screening-level experimental terrain-performance evaluation of an RHex-inspired hexapod robot using a fixed encoder-assisted alternating-tripod state-machine gait. The work aims to provide an experimentally grounded assessment of how terrain properties and practical leg-thickness variation influence the locomotion of a fabricated low-complexity legged platform. A 2 × 2 × 2 full-factorial screening design was adopted to evaluate terrain roughness, terrain compliance, and leg thickness. Four terrain conditions were tested: concrete, rocky terrain, foam mats, and grass, corresponding to smooth–rigid, rough–rigid, smooth–soft, and rough–soft surfaces, respectively. Each treatment combination was evaluated in two replicate runs using final forward displacement, lateral displacement, absolute displacement, and peak current as the response variables. The full-factorial analysis showed that terrain roughness had the clearest significant effect on forward displacement and absolute displacement, while terrain compliance significantly affected absolute displacement and showed observable trends in lateral displacement and peak current. Leg thickness did not produce a statistically significant main effect within the tested 2.5 mm and 5.0 mm configurations, fixed gait, and terrain set. The findings indicate that, for this platform and experimental scope, terrain roughness and compliance affected locomotion more strongly than the tested morphology variation. The study contributes a reproducible baseline workflow for terrain-performance evaluation in low-complexity legged robots and identifies directions for future work involving stronger replication, quantified terrain characterization, improved energy measurement, and closed-loop terrain-adaptive control. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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55 pages, 3145 KB

Open AccessSystematic Review

Systematic Literature Review of Vision-Language-Action Models for Generalist Robots

by Umair Cheema, Youakim Badr, Thao Minh Le and Katie Fitzsimons

Robotics 2026, 15(8), 160; https://doi.org/10.3390/robotics15080160 - 17 Aug 2026

Generalist robots need to perform diverse tasks while operating in dynamic, uncertain, and unstructured environments, often around human beings. Vision-language-action (VLA) models have recently emerged as a promising and flexible framework for integrating perception, reasoning, robotic control, and action execution to develop generalist [...] Read more.

Generalist robots need to perform diverse tasks while operating in dynamic, uncertain, and unstructured environments, often around human beings. Vision-language-action (VLA) models have recently emerged as a promising and flexible framework for integrating perception, reasoning, robotic control, and action execution to develop generalist robotic policies. This systematic literature review (SLR) examines more than 140 VLA-related publications between 2020 and 2025 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To the best of our knowledge, it is the first PRISMA-compliant systematic review dedicated to VLA models, offering a structured discussion of robotic policies, VLA architectures, and inference optimization methods. The review also presents descriptive analyses of the included studies and a glossary defining the terminology commonly used in VLA and generalist robotic policy research. The findings reveal substantial diversity among VLA models in terms of their supported modalities, robotic embodiments, training strategies, and architectural designs. Despite the rapid growth of VLA research, several important areas remain underexplored, including the execution of complex, long-horizon tasks, effective integration of speech, and deployment on low-cost hardware, while ensuring robust, safe, and secure operation. Full article

(This article belongs to the Section AI in Robotics)

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46 pages, 3342 KB

Open AccessReview

Advances in Pneumatic Upper-Limb Rehabilitation Robots: A Critical Review of Structural Design, Human–Robot Interaction, and Clinical Translation

by Yonggen Zhao, Yeming Zhang, Maolin Cai and Feng Wei

Robotics 2026, 15(8), 159; https://doi.org/10.3390/robotics15080159 - 14 Aug 2026

Upper-limb motor dysfunction resulting from neurological disorders severely limits patients’ activities of daily living and social participation. Pneumatic upper-limb rehabilitation robots have emerged as a promising intervention owing to their inherent compliance, lightweight design, and high power-to-weight ratio, which facilitate safe, repetitive, and [...] Read more.

Upper-limb motor dysfunction resulting from neurological disorders severely limits patients’ activities of daily living and social participation. Pneumatic upper-limb rehabilitation robots have emerged as a promising intervention owing to their inherent compliance, lightweight design, and high power-to-weight ratio, which facilitate safe, repetitive, and home-based training. Despite these advantages, extensive clinical translation remains hindered by challenges including actuator hysteresis, nonlinear dynamics, limited accuracy in intention recognition, and inconsistent clinical evaluation metrics. This review systematically examines recent advancements in pneumatic upper-limb rehabilitation robots across four critical dimensions: structural design, human–robot interaction, control strategies, and clinical translation. We comparatively analyze rigid exoskeletons, soft wearable devices, and rigid–soft hybrid configurations based on output capability, motion accuracy, comfort, and clinical applicability. The findings suggest that while rigid systems offer high precision and soft systems maximize safety, rigid–soft hybrid architectures represent a critical developmental trend for balancing motion accuracy with interaction compliance. Furthermore, the review evaluates multimodal sensing techniques (e.g., EMG, EEG, and IMUs) for motion intention decoding and training state monitoring, alongside conventional, adaptive, and artificial intelligence-driven control methods aimed at compensating for pneumatic nonlinearity and improving real-time response. Current clinical evidence indicates that these systems effectively enhance upper-limb function and muscle strength, particularly in post-stroke rehabilitation; however, existing trials are frequently constrained by small sample sizes, short interventions, and heterogeneous protocols. Future research must prioritize rigid–soft hybrid architectures, robust multimodal sensor fusion, digital twin-assisted assessment, adaptive intelligent control, and standardized home-based rehabilitation platforms. Ultimately, this comprehensive review provides a concise reference for the design optimization and clinical deployment of next-generation pneumatic rehabilitation systems. Full article

(This article belongs to the Section Medical Robotics and Service Robotics)

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23 pages, 10484 KB

Open AccessArticle

A Methodology for Early User Experience Evaluation of Large-Scale Collaborative Robot Applications

by Markus Nieradzik, Verena Staab, Adjie Salman and Dieter Schramm

Robotics 2026, 15(8), 158; https://doi.org/10.3390/robotics15080158 - 14 Aug 2026

When implementing collaborative robot applications, it is paramount to use a human-centered development approach to ensure a positive user experience and increase acceptance. User Experience (UX) design methods involve validating user experience through evaluations as a basic principle. The earlier UX evaluations are [...] Read more.

When implementing collaborative robot applications, it is paramount to use a human-centered development approach to ensure a positive user experience and increase acceptance. User Experience (UX) design methods involve validating user experience through evaluations as a basic principle. The earlier UX evaluations are carried out, the greater the added value that can be achieved. For collaborative robot applications, especially those involving large robot systems, these early evaluations are challenging since the entire application will not be available until the final stages of development. The proposed methodological approach to this problem utilizes a rudimentary, scaled test setup for early UX evaluations of the entire robot application, enabling user feedback to be incorporated into the development process early on. The method was applied in a project that developed a collaborative robot application for semi-automated liquid cargo handling in inland navigation. UX assessments were carried out using both the rudimentary test setup and a full-scale prototype in a later development phase. The comparison of both assessments proves the applicability of the proposed methodology. Serving as an overarching framework, this methodology encourages developers to test the entire collaborative robot application in user studies at an early stage, thereby gathering valuable user feedback. Full article

(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)

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20 pages, 14658 KB

Open AccessArticle

GeoHash3D: Robust and Efficient 6-DoF Pose Estimation from 3D Marker Sets

by Lijiu Wang, Kailas Mahalinga Upadhyaya, Oguz Kedilioglu, Michael Hofmann, Weimin Gan, Nico Hempel, Peter Mayr, Sebastian Reitelshöfer and Jörg Franke

Robotics 2026, 15(8), 157; https://doi.org/10.3390/robotics15080157 - 13 Aug 2026

Accurately measuring the six degrees of freedom (6-DoF) pose of a sample is a critical prerequisite for applications in robotic sample handling, optical metrology, and industrial quality control. Our pose estimation problem requires matching a small, partial observation of 4–20 discrete 3D points [...] Read more.

Accurately measuring the six degrees of freedom (6-DoF) pose of a sample is a critical prerequisite for applications in robotic sample handling, optical metrology, and industrial quality control. Our pose estimation problem requires matching a small, partial observation of 4–20 discrete 3D points to a reference point set of up to 50 points. Popular registration algorithms such as Go-ICP, TEASER++, and MAC are designed for large-scale correspondence problems and do not perform reliably in our operating regime. Therefore, we propose an improved geometric hashing method that robustly estimates the rigid transformation in the presence of noise and outliers, and demonstrate its effectiveness and speed using both simulated and real-world datasets. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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52 pages, 3640 KB

Open AccessSystematic Review

Multi-Agent Reinforcement Learning for Cooperative Manipulation in Industrial Robotics: A Systematic Review of Trends, Gaps and Research Drivers

by Francisco J. Huertos, Oihane Bañales, Pedro Alvarez and Itziar Cabanes

Robotics 2026, 15(8), 156; https://doi.org/10.3390/robotics15080156 - 12 Aug 2026

Modern manufacturing faces increasing demands for flexibility, customization, and productivity under dynamic conditions. Multi-robot systems offer a promising solution by enabling cooperative execution of complex tasks, such as assembly and cooperative manipulation. In this context, Multi-Agent Reinforcement Learning (MARL) has emerged as a [...] Read more.

Modern manufacturing faces increasing demands for flexibility, customization, and productivity under dynamic conditions. Multi-robot systems offer a promising solution by enabling cooperative execution of complex tasks, such as assembly and cooperative manipulation. In this context, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising paradigm to enhance coordination and adaptability in industrial settings. MARL enables multiple agents to learn and interact in shared environments to achieve common goals within complex and dynamic industrial processes. In this paper, a deep analysis of MARL applied to industrial multi-robot systems based on a systematic review is presented, with particular focus on cooperative manipulation tasks. Following PRISMA guidelines, we analyze a total of 30 articles published between 2016 and 2026, selected independently by two of the authors from an initial pool of 102 records retrieved from Scopus and Web of Science. These articles were used to address five key questions regarding MARL algorithms, control architectures, industrial applications and validation practices. These research questions seek to examine gaps and trends at the research level which are important for the development of multi-agent control technologies. This review shows a clear prevalence of model-free algorithms under Centralized Training with Decentralized Execution (CTDE) architectures, with validation mainly performed in simulation. Despite promising results and high potential for impact, critical gaps remain in scalability, reproducibility, and sim-to-real transfer, limiting real deployment in manufacturing environments. To address these challenges and fill current gaps, we outline actionable research directions, such as hybrid MARL approaches, standardized industrial benchmarks, digital twin pipelines, and safety-aware deployment strategies, to accelerate MARL adoption in industrial environments. Full article

(This article belongs to the Section Industrial Robots and Automation)

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22 pages, 5276 KB

Open AccessReview

Pursuit–Evasion Strategies in Multi-Agent Robotic Systems: Analytical, Learning-Based, and Evolutionary Perspectives

by Alejandro Moreno-Martinez, Victor Landassuri-Moreno, Asdrúbal López-Chau, Saul Lazcano-Salas and Heriberto Casarrubias-Vargas

Robotics 2026, 15(8), 155; https://doi.org/10.3390/robotics15080155 - 12 Aug 2026

In studies of intelligent agents, the pursuit–evasion problem, commonly related to the predator–prey paradigm, has been used as a reference setting for examining decision-making, coordination, and adaptation in multi-agent systems. In this paper, pursuit and evasion strategies are reviewed from their theoretical foundations [...] Read more.

In studies of intelligent agents, the pursuit–evasion problem, commonly related to the predator–prey paradigm, has been used as a reference setting for examining decision-making, coordination, and adaptation in multi-agent systems. In this paper, pursuit and evasion strategies are reviewed from their theoretical foundations to the gradual incorporation of adaptive methods based on evolutionary algorithms and machine learning. Analytical formulations drawn from control theory, game theory, and graph-based models are considered together with learning-oriented methods, including multi-agent reinforcement learning, neuroevolution, evolutionary robotics, and competitive coevolution. The literature is arranged by methodological paradigm so that the scope, limitations, and applicability of each approach can be discussed in relation to dynamic and uncertain environments. Through this organization, classical models, algorithmic developments, and recent research trends are brought into the same discussion, while relevant gaps and possible future directions in pursuit–evasion research are identified. The contribution of this work is a structured synthesis in which analytical and adaptive perspectives are brought together within a unified reference for researchers and practitioners in robotics, artificial intelligence, and multi-agent systems. Full article

(This article belongs to the Section AI in Robotics)

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35 pages, 12457 KB

Open AccessArticle

ActivAsk: Free-Energy-Guided Clarification for Robotic Grasping Under Ambiguous Instructions

by Haoandong Yang, Gabriel W. Haddon-Hill, Teresa Zielinska and Shingo Murata

Robotics 2026, 15(8), 154; https://doi.org/10.3390/robotics15080154 - 11 Aug 2026

Service robots often receive natural language instructions in changing workspaces where multiple visible objects may match one description. Relying on detector confidence, random selection, or direct vision–language model (VLM) prediction can lead to a wrong action. This paper presents ActivAsk, a zero-shot framework [...] Read more.

Service robots often receive natural language instructions in changing workspaces where multiple visible objects may match one description. Relying on detector confidence, random selection, or direct vision–language model (VLM) prediction can lead to a wrong action. This paper presents ActivAsk, a zero-shot framework for resolving referential ambiguity before robotic grasping. ActivAsk constructs open-vocabulary candidates from red-green-blue-depth (RGB-D) input, asks candidate-grounded yes/no questions when needed, updates the candidate state from the user’s answer, and grasps after target resolution. It selects among VLM-proposed candidate partitions using an expected free energy (EFE) criterion motivated by active inference; with neutral response preferences, this reduces to information gain over candidate partitions. Offline experiments showed that interactive clarification improved target accuracy from about 53–54% for noninteractive baselines to about 90–92%. ActivAsk matched the best interactive accuracy (92.13%) while asking 15.47–19.71% fewer questions on asked trials and 21.43–23.88% fewer for ambiguous instructions. In online real robot experiments, ActivAsk achieved 92.98% target selection accuracy and 87.72% full correct object grasp success; unresolved or wrong targets were not physically executed after operator-controlled verification and were counted as task failures. Full article

(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)

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23 pages, 441 KB

Open AccessArticle

Contact-Aware Predictive Control of a Logarithmic-Spiral Soft Gripper: A Control-Oriented Reduced-Order Numerical Study

by Daniel Sanin-Villa, Vanessa Botero-Gómez and Adrián Felipe Martínez Pérez

Robotics 2026, 15(8), 153; https://doi.org/10.3390/robotics15080153 - 11 Aug 2026

Logarithmic-spiral soft grippers couple tendon actuation, variable-curvature morphology, distributed contact, and frictional load support. This study evaluates a finite-candidate predictive force-shape (PFS) controller within a control-oriented reduced-order surrogate of a two-tendon gripper. PFS is compared with open-loop, fixed-tension, position-only, and hybrid force-shape controllers [...] Read more.

Logarithmic-spiral soft grippers couple tendon actuation, variable-curvature morphology, distributed contact, and frictional load support. This study evaluates a finite-candidate predictive force-shape (PFS) controller within a control-oriented reduced-order surrogate of a two-tendon gripper. PFS is compared with open-loop, fixed-tension, position-only, and hybrid force-shape controllers across multiple object geometries, simultaneous uncertainty, payload-friction conditions, transient loads, ablations, and parameter variations. In the nominal study, PFS produced a mean force RMSE of 7.26 N and a mean peak local force of 8.07 N, while the comparison implementations produced force RMSE values from

57.9

N to

99.3

N and peak forces near

30.6

N. This behavior involved a geometric tradeoff: PFS position RMSE was

0.088

m, compared with

0.073

m for PO and

0.074

m for HFS. The remaining numerical studies characterize how this tradeoff changes inside the surrogate. A matched higher-resolution verification at

(N, N_{c}) = (60, 48)

preserved the principal force–position tradeoff: PFS yielded a mean force RMSE of

10.25

N and peak local force of

8.84

N, while PO and HFS retained lower position RMSE. Because the morphology and contact relations are phenomenological, the results are interpreted as reproducible numerical evidence rather than experimental validation or proof of physical superiority. Full article

(This article belongs to the Section Soft Robotics)

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29 pages, 8728 KB

Open AccessSystematic Review

Socially Assistive Robots for Cognitive Assessment and Training in Older Adults: A Systematic Review

by Muhammad Zubair, Christian Tamantini, Riccardo De Benedictis, Francesca Fracasso, Alessandro Umbrico, Gloria Beraldo, Andrea Orlandini and Gabriella Cortellessa

Robotics 2026, 15(8), 152; https://doi.org/10.3390/robotics15080152 - 11 Aug 2026

Cognitive assessment and training are fundamental tools for supporting healthy aging and monitoring cognitive decline in older adults. Socially assistive robots are being investigated in this context because of their potential to provide multimodal, engaging, and personalized interactions during cognitive interventions. However, current [...] Read more.

Cognitive assessment and training are fundamental tools for supporting healthy aging and monitoring cognitive decline in older adults. Socially assistive robots are being investigated in this context because of their potential to provide multimodal, engaging, and personalized interactions during cognitive interventions. However, current evidence remains fragmented regarding the robotic embodiments, interaction strategies, and monitoring approaches used for cognitive assessment and training, as well as their ability to produce robust and long-term clinical benefits. To address this gap, this systematic review analyzes 37 studies identified through searches of Scopus, PubMed, and IEEE Xplore, following a PRISMA-based methodology. The review examines the main technological and methodological characteristics of the proposed systems, including robot embodiment, interaction capabilities, cognitive intervention paradigms, and the integration of multimodal monitoring technologies. The results highlight the predominance of humanoid robots and multimodal interaction strategies, together with an emerging use of adaptive and AI-based approaches. However, the available evidence is characterized by methodological heterogeneity, limited longitudinal validation, and a lack of standardized and ecologically valid evaluation protocols. Although the reviewed studies report encouraging findings concerning feasibility, engagement, acceptance, and preliminary cognitive outcomes, the evidence remains insufficient to identify the most effective robotic embodiment or confirm long-term clinical benefits. Larger comparative and longitudinal studies are therefore required. Full article

(This article belongs to the Special Issue From Research to Healthcare: Translational Advances in Robotic Rehabilitation, Assistance, and Prosthetics)

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26 pages, 43560 KB

Open AccessArticle

Third-Person Views and Enhanced Visual Feedback for Precision Telemanipulation: A Human-Centred Operator Study

by Advay Kumar, Yuyang Ji, Pamela Carreno-Medrano and Akansel Cosgun

Robotics 2026, 15(8), 151; https://doi.org/10.3390/robotics15080151 - 10 Aug 2026

Remote teleoperation interfaces for precision manipulation should support operator awareness and usability while preserving human control, consistent with the human-centred goals of Industry 5.0. This study conducted a within-subject user evaluation with 18 participants using a UR5 arm. Three interface conditions were compared: [...] Read more.

Remote teleoperation interfaces for precision manipulation should support operator awareness and usability while preserving human control, consistent with the human-centred goals of Industry 5.0. This study conducted a within-subject user evaluation with 18 participants using a UR5 arm. Three interface conditions were compared: an enhanced first-person interface with visual overlays (FPV+), a multi-view interface combining first-person and third-person views (PiP), and a multi-view interface with enhanced visual overlays (PiP+). Participants completed a ball task and a pen insertion task in pseudo-randomised order. Binary task success was analysed using generalised linear mixed-effects models, while System Usability Scale scores were analysed using a linear mixed-effects model. Neither adding a third-person view to the enhanced first-person interface nor adding the minimap and tactile-dot overlays to the multi-view interface was significantly associated with task success. A post hoc sensitivity analysis showed that these binary-success comparisons were sensitive only to large effects, so they are interpreted as inconclusive rather than as evidence of absence. Success was significantly lower for the pen task, and its relationship with prior human–robot interaction experience differed from that observed for the ball task. Adding a third-person view was associated with an approximately 16-point higher System Usability Scale score, whereas the minimap and tactile-dot overlays were not significantly associated with usability. These findings illustrate a human-centred evaluation principle relevant to Industry 5.0: an interface feature can improve the operator’s experience of a teleoperation system even when no corresponding change in objective task success is detectable, revealing a benefit that a performance-only evaluation might overlook. Full article

(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)

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20 pages, 14935 KB

Open AccessArticle

Collision Avoidance with Deep Learning in a Digital Twin for Industrial Collaborative Robot Manipulation

by Tuan-Khanh Nguyen, The-Thinh Pham and Chi-Cuong Tran

Robotics 2026, 15(8), 150; https://doi.org/10.3390/robotics15080150 - 6 Aug 2026

Safe human–robot collaboration remains a critical challenge in manufacturing. Traditional safety approaches, such as cages and proximity sensors, are often insufficient for dynamic human interaction. This paper presents a digital twin-based collision avoidance framework for industrial collaborative robot manipulation. The system integrates RGB-D [...] Read more.

Safe human–robot collaboration remains a critical challenge in manufacturing. Traditional safety approaches, such as cages and proximity sensors, are often insufficient for dynamic human interaction. This paper presents a digital twin-based collision avoidance framework for industrial collaborative robot manipulation. The system integrates RGB-D sensing, human pose estimation using Ultralytics YOLO26s-pose, Kalman-filter-based 3D arm tracking, short-term motion prediction, and QP-based reactive motion control. Human arm keypoints detected from RGB-D images are reconstructed in 3D, transformed into the robot base frame, and tracked during temporary occlusion using Kalman filtering with kinematic constraints. Predicted human–robot clearance is evaluated to trigger speed reduction, stopping, or collision avoidance commands. The framework was implemented with a UR10e robot, an Intel RealSense D435 camera, a Unity3D digital twin, and ROS communication. Controlled laboratory experiments demonstrated the proof-of-concept feasibility of the integrated framework for tracking human arm motion, anticipating proximity risk, and triggering protective robot responses. The results do not establish deployment readiness in complex industrial or multi-participant environments. Full article

(This article belongs to the Section Industrial Robots and Automation)

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