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The integration of Augmented Reality (AR) into Human–Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI with a focus on manufacturing, emphasizing their role in enhancing collaboration, trust, and safety. By aggregating findings from numerous studies, this research highlights key challenges, including the need for improved Situational Awareness, enhanced safety, and more effective communication between humans and robots. A framework developed from the literature is presented, detailing the critical elements of AR necessary for advancing HRI. The framework outlines effective methods for continuously evaluating AR systems for HRI. The framework is supported with the help of two case studies and another ongoing research endeavor presented in this paper. This structured approach focuses on enhancing collaboration and safety, with a strong emphasis on integrating best practices from Human–Computer Interaction (HCI) centered around user experience and design. [ABSTRACT FROM AUTHOR]

Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23 % for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23 % for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208 mm , being 200 mm the limit safety distance set by algorithm. [ABSTRACT FROM AUTHOR]

The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from “how innovation fosters AI” to “how AI fosters innovation”. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI’s effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0. [ABSTRACT FROM AUTHOR]

Due to recent advances in mobile robotics, applications involving multiple robots have become the focus of research in this area. Communication and data exchange among robots are essential features of these systems. In this context, this work presents the conception and the implementation of an application used to share local three‐dimensional occupancy grid maps in a network composed of multiple unmanned aerial vehicles (UAVs). This system is based on compression, serialization, packaging, and data transmission whenever the maps receive updates. The simulations demonstrate that the application can significantly reduce the map data size and efficiently share it among all members of the cooperative system. Moreover, a scalability test demonstrates that the system is scalable with a growing number of UAVs. [ABSTRACT FROM AUTHOR]

Collaborative robotics is a field of growing industrial interest, within which understanding the energetic behavior of manipulators is essential. In this work, we present the electro-mechanical modeling of the UR5 e-series robot through the identification of its dynamics and electrical parameters. By means of the identified robot model, it is then possible to compute and optimize the energy consumption of the robot during prescribed trajectories. The proposed model is derived from data acquired from the robot controller during bespoke experimental tests, using model identification procedures and datasheet provided by manipulator, motors, and gearbox manufacturers. The entire procedure does not require the use of any additional sensor, so it can be easily replicated with an off-the-shelf manipulator, and applied to other robots of the same family. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts. [ABSTRACT FROM AUTHOR]

This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related disassembly tasks are characterized by variabilities such as rust, deformation, and diverse part geometries, traditional industrial robots face significant challenges in this domain. These challenges require adaptable and flexible automation solutions that can work safely alongside human workers. We developed an architecture to address these challenges and support system configuration, training, and operational monitoring. Our framework incorporates a digital twin to provide a real-time virtual representation of the physical disassembly process, allowing for immediate feedback and dynamic adjustment of operations. In addition, VR is used to simulate and optimize the workspace layout, improve human–robot interaction, and facilitate safe and effective training scenarios without the need for physical prototypes. A unique case study is presented, where the collaborative system is specifically applied to the disassembly of antenna amplifiers, illustrating the potential of our comprehensive approach to facilitate engineering processes and enhance collaborative safety. [ABSTRACT FROM AUTHOR]

In this paper, an approach for computing online safety zones for collaborative robotics in a robust way, despite uncertain robot dynamics, is proposed. The strategy implements the speed and separation monitoring paradigm, and considers human and robot enclosed in bounding volumes. The human-robot collaboration is monitored by a supervisory controller that guides the robot to stop along a path-consistent trajectory in case of collision danger between human and robot. The size of the robot safety zone is minimized online according to the stop time of the manipulator, and the uncertain robot dynamics is considered using interval arithmetic to ensure compliance with the joint torques limits even in case of imperfect knowledge of the dynamic model parameters. The results verify the effectiveness of the proposed approach, and evaluate the influence of dynamics variations on human-robot collaboration. [ABSTRACT FROM AUTHOR]

This paper explores and experimentally compares the effectiveness of robot-stopping approaches based on the speed and separation monitoring for improving fluency in collaborative robotics. In the compared approaches, a supervisory controller checks the distance between the bounding volumes enclosing human operator and robot and prevents potential collisions by determining the robot's stop time and triggering a stop trajectory if necessary. The methods are tested on a Franka Emika robot with 7 degrees of freedom, involving 27 volunteer participants, who are asked to walk along assigned paths to cyclically intrude the robot workspace, while the manipulator is working. The experimental results show that scaling online the dynamic safety zones is beneficial for improving fluency of human-robot collaboration, showing significant statistical differences with respect to alternative approaches. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field. [ABSTRACT FROM AUTHOR]

Nowadays, there is a worldwide demand to create new, simpler, and more intuitive methods for the manual programming of industrial robots. Gestures can allow the operator to interact with the robot more simply and naturally, as gestures are used in everyday life. The authors have developed and tested a gesture-based robot programming approach for part-handling applications. Compared to classic manual programming methods using jogging and lead-through, the gesture control method reduced wasted time by up to 70% and reduced the probability of operator error. In addition, the proposed method compares favorably with similar works in that the proposed approach allows one to write programs in the native programming language of the robot's controller and allows the operator to control the gripper of an industrial robot. [ABSTRACT FROM AUTHOR]

Over the last 5 years, IR-MALDESI-MS (Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry) has been demonstrated for use in a range of high-throughput biochemical and cellular assays with remarkable sample acquisition rates up to 22 Hz for a single 384-well assay plate. With such high single plate acquisition rates, the rate limiting step becomes how fast subsequent plates can be presented to the MS for analysis. To make this transfer as fast as possible while maintaining safe operation in a laboratory environment, we developed a collaborative robotic plate transfer system (CRPTS) that combines a 6-axis robot with dual plate grippers, a 7th axis conveyor stage, and a 420-plate capacity sample loading window. As a demonstration of the throughput and flexibility of CRPTS, we performed a biochemical assay that monitored the oxidation of tris(2-carboxyethyl)phosphine (TCEP) to screen for nuisance compounds. Using continuous and step motion scan profiles, we analyzed 158,799 compounds contained in 448 assay plates over the course of 12.5 h (Z-Factor=0.87) and 17.5 h (Z-factor=0.99), respectively. Extrapolating these results enables the screening of a million compounds within 6–7 working days.

Collaborative and mobile robotics for industrial environments promise to enable autonomous and flexible production processes. However, this vision also poses significant challenges to the robotic systems, requiring them to adapt to dynamic environments and ensure human safety by leveraging continuous image streams and sophisticated data processing. Edge computing allows offloading the computational load to edge servers, communicating the image data, generated on mobile robots, over fast and reliable private 5G networks. However, there are multidisciplinary and interdependent factors that influence the reaction time of the distributed system which are not well explored in the literature for real robotic use cases, but have a significant impact on safe robotic behavior and the effectiveness of edge computing. In this work, we implement a distributed control system that offloads the image processing to measure and analyze the effect of various factors on the reaction time of the system for collaborative robotics applications. Different values for the sensing rate, image resolution and compression and quality-of-service settings are evaluated for communication and computation times as well as for the task performance. To account for the safety requirements in collaborative robotics, we add a low-level control timeout in cases of large jitter and stop the robot in cases of frequently missed detections. A push and a teleoperation experiment evaluate the reaction times in real distributed control scenarios using 5G edge computing. All experiments are implemented using ROS 2 Humble. The code and videos of the experiments are available at https://github.com/DominikUrbaniak/ros2_distributed_control_system.

The integration of Augmented Reality (AR) into Human–Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI with a focus on manufacturing, emphasizing their role in enhancing collaboration, trust, and safety. By aggregating findings from numerous studies, this research highlights key challenges, including the need for improved Situational Awareness, enhanced safety, and more effective communication between humans and robots. A framework developed from the literature is presented, detailing the critical elements of AR necessary for advancing HRI. The framework outlines effective methods for continuously evaluating AR systems for HRI. The framework is supported with the help of two case studies and another ongoing research endeavor presented in this paper. This structured approach focuses on enhancing collaboration and safety, with a strong emphasis on integrating best practices from Human–Computer Interaction (HCI) centered around user experience and design.

Incorporating collaborative applications constitutes a challenging and increasingly intricate objective within the context of small and medium-sized enterprises (SMEs). This challenge stems from the shortage of highly specialized personnel in these types of companies when it comes to adopting cutting-edge technologies. The lack of innovation in production processes, however, increases the risk that SMEs will not be able to adapt to rapid changes in the market and the growing needs of consumers, who today are evolving at an unprecedented pace. The importance of adopting collaborative applications can be found in their capacity to harmonize human adaptability with the precision of robotic technology. This synergy contributes to the establishment of a safer work environment while guaranteeing effective and efficient performance. These features not only lead to improved production line performance compared to traditional manual or stationary operations, but also highlight new perspectives in the design, production, and customization of new products. This, in turn, helps companies strengthen their competitiveness in the global market. In this scenario, the primary challenge centers around effectively putting these solutions into practice. Our research aims to highlight how significant benefits can be achieved, both in terms of performance improvements and economically, through the analysis of a simple yet illuminating case study. [ABSTRACT FROM AUTHOR]

Patina is the natural aging process that gives leather a unique, worn appearance. However, this patina effect can also be created artificially by using specific products to rub the leather surface. This research presents a new and innovative method to automate the process of creating patina on leather footwear using collaborative robots. These types of finishing tasks on medium size objects are delicate and often require adjustments by skilled craftsmen, adding complexity to the automation process. This new solution involves designing and implementing a collaborative tool specifically created to cultivate a patina finish on shoe surfaces. The system aims to speed up the process without compromising the quality of the finished product. In order to accomplish this task, a collaborative robot that can regulate contact force and a preexisting software feature for recording manual paths are utilized together with the skills of an expert craftsman. This enables the robot's end effector to conform to the curved contours of the shoe. Furthermore, the tool's path and position are modified based on the data from the CAD model to ensure precise outcomes. This study introduces a novel technique for automated and partially automated patina development in the shoe industry. This work presents a system integrated into a productive facility and it is currently being used. [ABSTRACT FROM AUTHOR]

In collaborative robotics, to improve human–robot interaction (HRI), it is necessary to avoid accidental impacts. In this direction, several works reported how to modify the trajectories of collaborative robots (cobots), monitoring the operator's position in the cobot workspace by industrial safety devices, cameras, or wearable tracking devices. The detection of the emotional state of the operator could further prevent possible dangerous situations. This work aimed to increase the predictability of anomalous behavior on the part of human operators by the implementation of emotional intelligence (EI) that allows a cobot to detect the operator's Level of Attention (LoA), implicitly associated with the emotional state, and to decide the safest trajectory to complete a task. Consequently, the operator is induced to pay due attention, the safety rate of the HRI is improved, and the cobot downtime is reduced. The approach was based on a vision transformer (ViT) architecture trained and validated by the Level of Attention Dataset (LoAD), the ad hoc dataset created and developed on facial expressions and hand gestures. ViT was integrated into a digital twin of the Omron TM5-700 cobot, suitably developed within this project, and the effectiveness of the EI was tested on a pick-and-place task. Then, the proposed approach was experimentally validated with the physical cobot. The results of the simulation and experimentation showed that the goal of the work was achieved and the decision-making process can be successfully integrated into existing robot control strategies. [ABSTRACT FROM AUTHOR]

The human-centric approach is a leading trend for future production processes, and collaborative robotics are key to its realization. This article addresses the challenge of designing a new custom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using "general-purpose standard" safety and motion control technologies. This is conducted on a non-conventional cable-driven parallel robot (CDPR). Safety is assured by safe commands to individual axes, known as safe motion monitoring functionalities, which limit the axis's speed in the event of human intrusion. At the same time, the robot's motion controller applies an override to the toolpath speed to accommodate the robot's path speed to the limitations of the axes. The implementation of a new Pre-Warning Zone prevents unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance. [ABSTRACT FROM AUTHOR]

The manufacturing landscape is rapidly evolving, being characterized by the emergence of Industry 5.0, leading to a growing recognition of the importance of a human-centered, sustainable and resilient approach. In response to these needs, collaborative robotics has emerged as a promising solution to enhance flexibility in industrial operations, while ensuring a higher level of safety. Cobots enable human-robot collaboration without the need for physical barriers, reduce the footprint of the robotic station, and foster a closer working relationship between humans and machines. This article focuses on an application of collaborative robotics in end-of-line industrial operations, specifically in the unloading of products from an automatic machine. By managing repetitive tasks, such as placing the trays near the end of the line of the automatic machine, waiting for them to be filled, and placing them on a trolley, which will then be moved by the human operator, the robot significantly improves efficiency. The proposed solution involves alternating the placement of trays in two rack trolleys, allowing the worker to replace the full trolleys with empty ones at his own timing. The paper examines the potential advantages of using a human-centric approach that emphasizes operator empowerment and human oversight: the collaborative robotic cell and the automation implemented free the workers from the low value-added operations and allow them to oversee and manage multiple machines simultaneously, thus enhancing productivity. The approach behind this proposal is to keep robotic tasks simple, easily reprogrammable and modular, while empowering the human operator to engage in supervision, decision-making, and problem-solving tasks. This collaboration allows the operator to assume a supervisory role, organizing production based on identified needs, and elevating their involvement to production line management tasks. [ABSTRACT FROM AUTHOR]

Human–robot collaboration has gained attention in the field of manufacturing and assembly tasks, necessitating the development of adaptable and user-friendly forms of interaction. To address this demand, collaborative robots (cobots) have emerged as a viable solution. Deep Learning has played a pivotal role in enhancing robot capabilities and facilitating their perception and understanding of the environment. This study proposes the integration of cobots and Deep Learning to assist users in assembly tasks such as part handover and storage. The proposed system includes an object classification system to categorize and store assembly elements, a voice recognition system to classify user commands, and a hand-tracking system for close interaction. Tests were conducted for each isolated system and for the complete application as used by different individuals, yielding an average accuracy of 91.25%. The integration of Deep Learning into cobot applications has significant potential for transforming industries, including manufacturing, healthcare, and assistive technologies. This work serves as a proof of concept for the use of several neural networks and a cobot in a collaborative task, demonstrating communication between the systems and proposing an evaluation approach for individual and integrated systems. [ABSTRACT FROM AUTHOR]

Collaborative robotics represents a modern and efficient framework in which machines can safely interact with humans. Coupled with artificial intelligence (AI) systems, collaborative robots can solve problems that require a certain degree of intelligence not only in industry but also in the entertainment and educational fields. Board games like chess or checkers are a good example. When playing these games, a robotic system has to recognize the board and pieces and estimate their position in the robot reference frame, decide autonomously which is the best move to make (respecting the game rules), and physically execute it. In this paper, an intelligent and collaborative robotic system is presented to play Italian checkers. The system is able to acquire the game state using a camera, select the best move among all the possible ones through a decision-making algorithm, and physically manipulate the game pieces on the board, performing pick-and-place operations. Minimum-time trajectories are optimized online for each pick-and-place operation of the robot so as to make the game more fluent and interactive while meeting the kinematic constraints of the manipulator. The developed system is tested in a real-world setup using a Franka Emika arm with seven degrees of freedom. The experimental results demonstrate the feasibility and performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Manufacturing assembly tasks can vary in complexity and level of automation. Yet, achieving full automation can be challenging and inefficient, particularly due to the complexity of certain assembly operations. Human-robot collaborative work, leveraging the strengths of human labor alongside the capabilities of robots, can be a solution for enhancing efficiency. This paper introduces the CT benchmark, a benchmark and model set designed to facilitate the testing and evaluation of human-robot collaborative assembly scenarios. It was designed to compare manual and automatic processes using metrics such as the assembly time and human workload. The components of the model set can be assembled through the most common assembly tasks, each with varying levels of difficulty. The CT benchmark was designed with a focus on its applicability in human-robot collaborative environments, with the aim of ensuring the reproducibility and replicability of experiments. Experiments were carried out to assess assembly performance in three different setups (manual, automatic and collaborative), measuring metrics related to the assembly time and the workload on human operators. The results suggest that the collaborative approach takes longer than the fully manual assembly, with an increase of 70.8%. However, users reported a lower overall workload, as well as reduced mental demand, physical demand, and effort according to the NASA-TLX questionnaire.

The featured dataset, the Event-based Dataset of Assembly Tasks (EDAT24), showcases a selection of manufacturing primitive tasks (idle, pick, place, and screw), which are basic actions performed by human operators in any manufacturing assembly. The data were captured using a DAVIS240C event camera, an asynchronous vision sensor that registers events when changes in light intensity value occur. Events are a lightweight data format for conveying visual information and are well-suited for real-time detection and analysis of human motion. Each manufacturing primitive has 100 recorded samples of DAVIS240C data, including events and greyscale frames, for a total of 400 samples. In the dataset, the user interacts with objects from the open-source CT-Benchmark in front of the static DAVIS event camera. All data are made available in raw form (.aedat) and in pre-processed form (.npy). Custom-built Python code is made available together with the dataset to aid researchers to add new manufacturing primitives or extend the dataset with more samples.

This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related disassembly tasks are characterized by variabilities such as rust, deformation, and diverse part geometries, traditional industrial robots face significant challenges in this domain. These challenges require adaptable and flexible automation solutions that can work safely alongside human workers. We developed an architecture to address these challenges and support system configuration, training, and operational monitoring. Our framework incorporates a digital twin to provide a real-time virtual representation of the physical disassembly process, allowing for immediate feedback and dynamic adjustment of operations. In addition, VR is used to simulate and optimize the workspace layout, improve human–robot interaction, and facilitate safe and effective training scenarios without the need for physical prototypes. A unique case study is presented, where the collaborative system is specifically applied to the disassembly of antenna amplifiers, illustrating the potential of our comprehensive approach to facilitate engineering processes and enhance collaborative safety.

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts.