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Robotics
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Human–Robot Collaboration in Industry 5.0
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Human–Robot Collaboration in Industry 5.0

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Industrial Robots and Automation".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 9401

Editor

Dr. Stefano Baraldo

E-Mail Website
Guest Editor
Automation, Robotics and Machines, Institute of Systems and Technology for Sustainable Production, Department of Innovative Technologies, University of Applied Science and Arts of Southern Switzerland, Via La Santa 1, 6962 Lugano, Switzerland
Interests: industrial robotics; metal additive manufacturing; machine learning; human–robot collaboration

Special Issue Information

Dear Colleagues,

Industry is moving towards new models of human–machine interaction, where workers are put at the center of an ecosystem that supports them in achieving both productivity and wellbeing, rather than being overwhelmed by technology. In this framework, called Industry 5.0, the new generation of robots must be able to truly collaborate with workers, thanks to safe, natural, and ultimately ethical interactions.

We are pleased to invite you to submit an article to this Special Issue, which is aimed at promoting actual developments in human–robot collaboration (HRC) and their main applications in industrial settings, where safety, trust, but also efficiency have paramount importance.

The Special Issue aims to collect relevant contributions about key topics in HRC, including but not limited to the following: safe shared-space interaction, learning by demonstration, natural language-based control, behavior programming and adaptation, affective computing, HRC experience evaluation, ethics in AI, edge computing, and HRC applications.

Dr. Stefano Baraldo
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics 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 1800 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–robot collaboration
  • affective computing
  • learning by demonstration
  • natural language processing
  • deliberation
  • behavior adaptation
  • AI ethics
  • industrial applications
  • voice interaction
  • computer vision

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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

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24 pages, 3856 KB  
Article
Human–Robot Interaction: External Force Estimation and Variable Admittance Control Incorporating Passivity
by Jun Wan, Zihao Zhou, Nuo Yun, Kehong Wang and Xiaoyong Zhang
Robotics 2026, 15(5), 84; https://doi.org/10.3390/robotics15050084 - 22 Apr 2026
Viewed by 653
Abstract
In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their [...] Read more.
In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their reliance on accurate dynamic models. To address these challenges, this paper proposes a sensorless external force estimation and variable admittance control method that models robot dynamic uncertainties and interaction forces as normally distributed stochastic quantities. An improved particle swarm optimization algorithm is introduced to calibrate the variance parameters, enhancing estimation accuracy and robustness. Furthermore, an energy-based variable admittance control strategy is developed, which preserves system passivity by adaptively adjusting inertia and damping gains based on real-time energy variations. The proposed method was validated on a redundant robot platform. Experimental results show that the external force and torque estimation errors remain below 3 N and 3 N.m, respectively, with lower detection delays and errors than those of a first-order generalized momentum observer in collision detection. Variable admittance experiments demonstrate that the system maintains passivity and stable interaction even under sudden arm stiffness changes. The approach is well-suited for industrial applications requiring safe, sensorless, and compliant human–robot collaboration. Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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20 pages, 7825 KB  
Article
STAG-Net: A Lightweight Spatial–Temporal Attention GCN for Real-Time 6D Human Pose Estimation in Human–Robot Collaboration Scenarios
by Chunxin Yang, Ruoyu Jia, Qitong Guo, Xiaohang Shi, Masahiro Hirano and Yuji Yamakawa
Robotics 2026, 15(3), 54; https://doi.org/10.3390/robotics15030054 - 4 Mar 2026
Viewed by 1366
Abstract
Most existing research in human pose estimation focuses on predicting joint positions, paying limited attention to recovering the full 6D human pose, which comprises both 3D joint positions and bone orientations. Position-only methods treat joints as independent points, often resulting in structurally implausible [...] Read more.
Most existing research in human pose estimation focuses on predicting joint positions, paying limited attention to recovering the full 6D human pose, which comprises both 3D joint positions and bone orientations. Position-only methods treat joints as independent points, often resulting in structurally implausible poses and increased sensitivity to depth ambiguities—cases where poses share nearly identical joint positions but differ significantly in limb orientations. Incorporating bone orientation information helps enforce geometric consistency, yielding more anatomically plausible skeletal structures. Additionally, many state-of-the-art methods rely on large, computationally expensive models, which limit their applicability in real-time scenarios, such as human–robot collaboration. In this work, we propose STAG-Net, a novel 2D-to-6D lifting network that integrates Graph Convolutional Networks (GCNs), attention mechanisms, and Temporal Convolutional Networks (TCNs). By simultaneously learning joint positions and bone orientations, STAG-Net promotes geometrically consistent skeletal structures while remaining lightweight and computationally efficient. On the Human3.6M benchmark, STAG-Net achieves an MPJPE of 41.8 mm using 243 input frames. In addition, we introduce a lightweight single-frame variant, STG-Net, which achieves 50.8 mm MPJPE while operating in real time at 60 FPS using a single RGB camera. Extensive experiments on multiple large-scale datasets demonstrate the effectiveness and efficiency of the proposed approach. Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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17 pages, 14849 KB  
Article
A Collaborative Robotic System for Autonomous Object Handling with Natural User Interaction
by Federico Neri, Gaetano Lettera, Giacomo Palmieri and Massimo Callegari
Robotics 2026, 15(3), 49; https://doi.org/10.3390/robotics15030049 - 27 Feb 2026
Viewed by 1251
Abstract
In Industry 5.0, the transition from fixed traditional automation to flexible human–robot collaboration (HRC) needs interfaces that are both intuitive and efficient. This paper introduces a novel, multimodal control system for autonomous object handling, specifically designed to enhance natural user interaction in dynamic [...] Read more.
In Industry 5.0, the transition from fixed traditional automation to flexible human–robot collaboration (HRC) needs interfaces that are both intuitive and efficient. This paper introduces a novel, multimodal control system for autonomous object handling, specifically designed to enhance natural user interaction in dynamic work environments. The system integrates a 6-Degrees of Freedom (DoF) collaborative robot (UR5e) with a hand-eye RGB-D vision system to achieve robust autonomy. The core technical contribution lies in a vision pipeline utilizing deep learning for object detection and point cloud processing for accurate 6D pose estimation, enabling advanced tasks such as human-aware object handover directly onto the operator’s hand. Crucially, an Automatic Speech Recognition (ASR) is incorporated, providing a Natural Language Understanding (NLU) layer that allows operators to issue real-time commands for task modification, error correction and object selection. Experimental results demonstrate that this multimodal approach offers a streamlined workflow aiming to improve operational flexibility compared to traditional HMIs, while enhancing the perceived naturalness of the collaborative task. The system establishes a framework for highly responsive and intuitive human–robot workspaces, advancing the state of the art in natural interaction for collaborative object manipulation. Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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20 pages, 14885 KB  
Article
MultiPhysio-HRC: A Multimodal Physiological Signals Dataset for Industrial Human–Robot Collaboration
by Andrea Bussolan, Stefano Baraldo, Oliver Avram, Pablo Urcola, Luis Montesano, Luca Maria Gambardella and Anna Valente
Robotics 2025, 14(12), 184; https://doi.org/10.3390/robotics14120184 - 5 Dec 2025
Cited by 4 | Viewed by 2318
Abstract
Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a [...] Read more.
Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems. Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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12 pages, 3628 KB  
Article
A Dataset of Standard and Abrupt Industrial Gestures Recorded Through MIMUs
by Elisa Digo, Michele Polito, Elena Caselli, Laura Gastaldi and Stefano Pastorelli
Robotics 2025, 14(12), 176; https://doi.org/10.3390/robotics14120176 - 28 Nov 2025
Cited by 1 | Viewed by 1207
Abstract
Considering the human-centric approach promoted by Industry 5.0, safety becomes a crucial aspect in scenarios of human–robot interaction, especially when abrupt human movements occur due to inattention or unexpected circumstances. To this end, human motion tracking is necessary to promote a safe and [...] Read more.
Considering the human-centric approach promoted by Industry 5.0, safety becomes a crucial aspect in scenarios of human–robot interaction, especially when abrupt human movements occur due to inattention or unexpected circumstances. To this end, human motion tracking is necessary to promote a safe and efficient human–machine interaction. Literature datasets related to the industrial context generally contain controlled and repetitive gestures tracked with visual systems or magneto-inertial measurement units (MIMUs), without considering the occurrence of unexpected events that might cause operators’ abrupt movements. Accordingly, the aim of this paper is to present the dataset DASIG (Dataset of Standard and Abrupt Industrial Gestures) related to both standard typical industrial movements and abrupt movements registered through MIMUs. Sixty healthy working-age participants were asked to perform standard pick-and-place gestures interspersed with unexpected abrupt movements triggered by visual or acoustic alarms. The dataset contains MIMUs signals collected during the execution of the task, data related to the temporal generation of alarms, anthropometric data of all participants, and a script for demonstrating DASIG usability. All raw data are provided, and the collected dataset is suitable for several analyses related to the industrial context (gesture recognition, motion planning, ergonomics, safety, statistics, etc.). Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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Review

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20 pages, 331 KB  
Review
Nonverbal Auditory Communication for Human–Robot Interaction in Industry 5.0: A Scoping Review
by Tom Schmid, Manja Lohse, Sven Winkelmann and Alexander von Hoffmann
Robotics 2026, 15(7), 121; https://doi.org/10.3390/robotics15070121 - 26 Jun 2026
Abstract
In Industry 5.0 (I5.0), close-proximity human–robot collaboration demands communication beyond conventional alarms and speech. Nonverbal auditory communication offers a complementary modality, yet its role in I5.0 remains unmapped. This scoping review maps nonverbal auditory communication research in I5.0 Human–Robot Interaction (HRI) and compares [...] Read more.
In Industry 5.0 (I5.0), close-proximity human–robot collaboration demands communication beyond conventional alarms and speech. Nonverbal auditory communication offers a complementary modality, yet its role in I5.0 remains unmapped. This scoping review maps nonverbal auditory communication research in I5.0 Human–Robot Interaction (HRI) and compares it with general HRI literature to identify transfer potential and research gaps. Peer-reviewed English-language articles (2023–April 2026) addressing nonverbal sound in HRI contexts were included. Speech, emotion detection, haptic interfaces and non-HRI domains were excluded. A search with two syntaxes across Web of Science, Scopus, IEEE Xplore, ACM and MDPI, supplemented by citation searching, targeted I5.0-specific (Syntax S1) and general HRI auditory literature (Syntax S2). This created two article record sets, n1 and n2. Articles were organized following Arksey and O’Malley’s framework and PRISMA-ScR into four inductively derived clusters: Sonification, Multimodal Feedback Systems, Safety and Frameworks and Concepts. From 782 initial records, 16 (n1) and 32 (n2) articles were included. In I5.0, multimodal feedback dominates: intentionally designed nonverbal sounds improve situational awareness, reduce cognitive workload and increase perceived safety. Compared to n2, which is shaped by social robotics and emotion-driven sound design, five gaps emerge in I5.0: absent emotion-related sound perception research, missing field studies, missing industry-specific sound design frameworks, underutilized sonification for spatial awareness and safety and no unimodal auditory studies under realistic industrial conditions. A dedicated sound design framework operationalizing I5.0 communicative requirements into designable sound parameters is needed, alongside empirical validation under realistic industrial noise conditions. Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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23 pages, 27743 KB  
Review
A Framework for Safe Mobile Manipulation in Human-Centered Applications
by Pangcheng David Cen Cheng, Cesare Luigi Blengini, Rosario Francesco Cavelli, Angela Ripi and Marina Indri
Robotics 2026, 15(4), 68; https://doi.org/10.3390/robotics15040068 - 25 Mar 2026
Viewed by 1128
Abstract
In recent years, applications with robots collaborating actively with humans have been increasing. The transition from Industry 4.0 to 5.0 rearranges the focus of fully automated processes to a human-centered system that allows more customization and flexibility. In human-centered systems, the robot is [...] Read more.
In recent years, applications with robots collaborating actively with humans have been increasing. The transition from Industry 4.0 to 5.0 rearranges the focus of fully automated processes to a human-centered system that allows more customization and flexibility. In human-centered systems, the robot is expected to safely assist or provide support to the human operator, avoiding any unintentional harm, while the latter is focused on tasks that require human reasoning, since current decision-making systems still have some limitations. This survey reviews all the main functionalities required to make a robot (collaborative or not) act as an assistant for human operators, analyzing and comparing solutions proposed by the authors (based on previous works) and/or the ones available in the literature. In this way, it is possible to combine those functionalities and build a complete framework enabling safe mobile manipulation while interacting with humans. In particular, a mobile manipulator is used to receive requests from a user, navigate in a human-shared environment, identify the requested object, and grasp and safely deliver such an object to the user. The framework, which is completed by a user interface designed using Android Studio, is developed in ROS1, tested, and validated on a real mobile manipulator in real-world conditions. Full article
(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)
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