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This paper presents a novel interaction planning method that exploits impedance tuning techniques in response to environmental uncertainties and unpredictable conditions using haptic information only. The proposed algorithm plans the robot's trajectory based on the haptic interaction with the environment and adapts planning strategies as needed. Two approaches are considered: Exploration and Bouncing strategies. The Exploration strategy takes the actual motion of the robot into account in planning, while the Bouncing strategy exploits the forces and the motion vector of the robot. Moreover, self-tuning impedance is performed according to the planned trajectory to ensure compliant contact and low contact forces. In order to show the performance of the proposed methodology, two experiments with a torque-controller robotic arm are carried out. The first considers a maze exploration without obstacles, whereas the second includes obstacles. The proposed method performance is analyzed and compared against previously proposed solutions in both cases. Experimental results demonstrate that: i) the robot can successfully plan its trajectory autonomously in the most feasible direction according to the interaction with the environment, and ii) a compliant interaction with an unknown environment despite the uncertainties is achieved. Finally, a scalability demonstration is carried out to show the potential of the proposed method under multiple scenarios., Comment: 8 pages, 9 figures, accepted for IEEE Robotics and Automation Letters (RA-L) and IEEE/RSJ International Conference on Intelligent Robots and Systems 2022

This work presents a reactive control strategy for loading and sudden unloading of an elbow effort-compensation device controlled in force. Through this control strategy, in addition to an individual's forearm weight, an external load can be detected and adaptively compensated via a feed-forward force reference, facilitating the execution of arbitrary movements by the wearer. In case of a sudden contact/load loss, a power-aware strategy is implemented to immediately eliminate the portion of external loading in the force reference. The adaptive compensation of the external loads is achieved through an electromyography interface. Instead, to react to sudden load releases, we set a power limit on the tendon, and continuously measure it through an encoder and a load cell connected with the cable. Two sets of experiments are designed to test the proposed load-releasing method on a bench-top setup with 2kg, and 3.9kg, and a human subject with 0.5and 1kg. Next, the overall scenario including load-compensation and load-releasing are carried out on eight human subjects with 0.5and 1kg loads to evaluate the release and compensation time, and the effort reduction with respect to non-powered exoskeleton case. Results show that the average compensation/release time (payload) among subjects is measured as 0.98/0.91seconds (0.5kg), and 1/0.86seconds (1kg). The average effort reduction among the subjects are also reported as 66.4%, and 67.11%for 0.5kg, and 1kg, respectively.

Vibration suppression is an important skill for future robots that will collaborate with humans in industrial settings. The vibration through physical interaction is a common problem in such settings, especially in operations involving hand-held vibrating tools. The existing human-robot collaboration (HRC) works addressing this problem mostly focus on the oscillations caused by the human operator, and suppress them by adapting the admittance parameters. This, however, usually results in stiffer robot behavior and contributes to reducing the overall performance of the task, in particular when impedance planning is a requirement. In this work, we focus on the vibration coming from external sources such as power tools and suppress it actively. We learn the vibration using the bandlimited multiple Fourier linear combiner (BMFLC) algorithm and apply it as a feedforward Cartesian force to cancel the vibration. We combine the feedforward force control with variable impedance learning and show that it improves the vibration suppression performance in simulation and real-world experiments. The feedforward approach can suppress the vibration better while keeping a more compliant set of impedance parameters, which is crucial in HRC.

Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless solutions. However, most of these methods operate in 2D, while those providing 3D solutions compromise accuracy and real-time performance. To address this challenge and unlock the potential of visual pose estimation methods in real-world scenarios, we propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion. Our approach integrates a Weighted Least Square (WLS) algorithm that computes 3D human motion from multiple 2D pose estimations provided by an AI-driven method. The method is integrated within the Open-VICO framework allowing simulation and real-world execution. Several experiments have been conducted, which have shown high accuracy and real-time performance, demonstrating the high level of readiness for real-world applications and the potential to revolutionize human motion capture., 19 pages, 7 figures

Human-robot collaborative disassembly is an emerging trend in the sustainable recycling process of electronic and mechanical products. It requires the use of advanced technologies to assist workers in repetitive physical tasks and deal with creaky and potentially damaged components. Nevertheless, when disassembling worn-out or damaged components, unexpected robot behaviors may emerge, so harmless and symbiotic physical interaction with humans and the environment becomes paramount. This work addresses this challenge at the control level by ensuring safe and passive behaviors in unplanned interactions and contact losses. The proposed algorithm capitalizes on an energy-aware Cartesian impedance controller, which features energy scaling and damping injection, and an augmented energy tank, which limits the power flow from the controller to the robot. The controller is evaluated in a real-world flawed unscrewing task with a Franka Emika Panda and is compared to a standard impedance controller and a hybrid force-impedance controller. The results demonstrate the high potential of the algorithm in human-robot collaborative disassembly tasks.

Human-robot collaborative disassembly is an emerging trend in the sustainable recycling process of electronic and mechanical products. It requires the use of advanced technologies to assist workers in repetitive physical tasks and deal with creaky and potentially damaged components. Nevertheless, when disassembling worn-out or damaged components, unexpected robot behaviors may emerge, so harmless and symbiotic physical interaction with humans and the environment becomes paramount. This work addresses this challenge at the control level by ensuring safe and passive behaviors in unplanned interactions and contact losses. The proposed algorithm capitalizes on an energy-aware Cartesian impedance controller, which features energy scaling and damping injection, and an augmented energy tank, which limits the power flow from the controller to the robot. The controller is evaluated in a real-world flawed unscrewing task with a Franka Emika Panda and is compared to a standard impedance controller and a hybrid force-impedance controller. The results demonstrate the high potential of the algorithm in human-robot collaborative disassembly tasks., 7 pages, 6 figures, accepted to the 2023 IEEE International Conference on Robotics and Automation (ICRA). Video available at https://www.youtube-nocookie.com/embed/SgYFHMlEl0k

Transporting large and heavy objects can benefit from Human-Robot Collaboration (HRC), increasing the contribution of robots to our daily tasks and reducing the risk of injuries to the human operator. This approach usually posits the human collaborator as the leader, while the robot has the follower role. Hence, it is essential for the leader to be aware of the environmental situation. However, when transporting a large object, the operator's situational awareness can be compromised as the object may occlude different parts of the environment. This paper proposes a novel haptic-based environmental awareness module for a collaborative transportation framework that informs the human operator about surrounding obstacles. The robot uses two LIDARs to detect the obstacles in the surroundings. The warning module alerts the operator through a haptic belt with four vibrotactile devices that provide feedback about the location and proximity of the obstacles. By enhancing the operator's awareness of the surroundings, the proposed module improves the safety of the human-robot team in co-carrying scenarios by preventing collisions. Experiments with two non-expert subjects in two different situations are conducted. The results show that the human partner can successfully lead the co-transportation system in an unknown environment with hidden obstacles thanks to the haptic feedback., 6 pages, 5 figures, submitted to IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO 2023), for associated video, see this https://youtu.be/UABeGPIIrHc

The growing deployment of human-robot collaborative processes in several industrial applications, such as handling, welding, and assembly, unfolds the pursuit of systems which are able to manage large heterogeneous teams and, at the same time, monitor the execution of complex tasks. In this paper, we present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs), in charge of actions scheduling, while the allocation problem is formulated through a Mixed-Integer Linear Program (MILP), that assigns dynamically individual roles or collaborations to the agents of the team. Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration (e.g. makespan, ergonomics, human preferences). Human preference are identified through a negotiation phase, in which, an human agent can accept/refuse to execute the assigned task.In addition, bilateral communication between humans and the system is achieved through an Augmented Reality (AR) custom user interface that provides intuitive functionalities to assist and coordinate workers in different action phases. The computational complexity of the proposed methodology outperforms literature approaches in industrial sized jobs and teams (problems up to 50 actions and 20 agents in the team with collaborations are solved within 1\;s). The different allocated roles, as the cost functions change, highlights the flexibility of the architecture to several production requirements. Finally, the subjective evaluation demonstrating the high usability level and the suitability for the targeted scenario., 16 pages, 20 fugures, submitted to Transaction on Robotics

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects., Comment: 8 pages, 8 figures, IEEE RAS International Symposium on Robot and Human Interactive Communication (RO-MAN), for associated video see https://youtu.be/Ftu_EHAtH4k

This document introduces the bridge between the leading inertial motion-capture systems for 3D human tracking and the most used robotics software framework. 3D kinematic data provided by Xsens are translated into ROS messages to make them usable by robots and a Unified Robotics Description Format (URDF) model of the human kinematics is generated, which can be run and displayed in ROS 3D visualizer, RViz. The code to implement the to-ROS-bridge is a ROS package called xsens_mvn_ros and is available on GitHub at https://github.com/hrii-iit/xsens_mvn_ros The main documentation can be found at https://hrii-iit.github.io/xsens_mvn_ros/index.html, Comment: 8 pages, 3 figures

Geometric navigation is nowadays a well-established field of robotics and the research focus is shifting towards higher-level scene understanding, such as Semantic Mapping. When a robot needs to interact with its environment, it must be able to comprehend the contextual information of its surroundings. This work focuses on classifying and localising objects within a map, which is under construction (SLAM) or already built. To further explore this direction, we propose a framework that can autonomously detect and localize predefined objects in a known environment using a multi-modal sensor fusion approach (combining RGB and depth data from an RGB-D camera and a lidar). The framework consists of three key elements: understanding the environment through RGB data, estimating depth through multi-modal sensor fusion, and managing artifacts (i.e., filtering and stabilizing measurements). The experiments show that the proposed framework can accurately detect 98% of the objects in the real sample environment, without post-processing, while 85% and 80% of the objects were mapped using the single RGBD camera or RGB + lidar setup respectively. The comparison with single-sensor (camera or lidar) experiments is performed to show that sensor fusion allows the robot to accurately detect near and far obstacles, which would have been noisy or imprecise in a purely visual or laser-based approach., Comment: Accepted to the 11th European Conference on Mobile Robots (ECMR) 2023

This manuscript introduces an object deformability-agnostic framework for co-carrying tasks that are shared between a person and multiple robots. Our approach allows the full control of the co-carrying trajectories by the person while sharing the load with multiple robots depending on the size and the weight of the object. This is achieved by merging the haptic information transferred through the object and the human motion information obtained from a motion capture system. One important advantage of the framework is that no strict internal communication is required between the robots, regardless of the object size and deformation characteristics. We validate the framework with two challenging real-world scenarios: co-transportation of a wooden rigid closet and a bulky box on top of forklift moving straps, with the latter characterizing deformable objects. In order to evaluate the generalizability of the proposed framework, a heterogenous team of two mobile manipulators that consist of an Omni-directional mobile base and a collaborative robotic arm with different DoFs is chosen for the experiments. The qualitative comparison between our controller and the baseline controller (i.e., an admittance controller) during these experiments demonstrated the effectiveness of the proposed framework especially when co-carrying deformable objects. Furthermore, we believe that the performance of our framework during the experiment with the lifting straps offers a promising solution for the co-transportation of bulky and ungraspable objects., 7 pages, 6 figures, accepted at IEEE International Conference on Robotics and Automation (ICRA 2023), for associated video, see https://youtu.be/Q3sA6YzTaaE

This paper proposes a hybrid optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the contact point between the robot and the object to minimize their relative velocity and reduce the impact forces. Next, the generated trajectories are updated by Kernelized Movement Primitives, which are based on human catching demonstrations to ensure a smooth transition around the catching point. In addition, the learned human variable stiffness (HVS) is sent to the robot's Cartesian impedance controller to absorb the post-impact forces and stabilize the catching position. Three experiments are conducted to compare our method with and without HVS against a fixed-position impedance controller (FP-IC). The results showed that the proposed methods outperform the FP-IC while adding HVS yields better results for absorbing the post-impact forces., 8 pages, 8 figures, submitted to 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

In this study, an adaptive object deformability-agnostic human-robot collaborative transportation framework is presented. The proposed framework enables to combine the haptic information transferred through the object with the human kinematic information obtained from a motion capture system to generate reactive whole-body motions on a mobile collaborative robot. Furthermore, it allows rotating the objects in an intuitive and accurate way during co-transportation based on an algorithm that detects the human rotation intention using the torso and hand movements. First, we validate the framework with the two extremities of the object deformability range (i.e, purely rigid aluminum rod and highly deformable rope) by utilizing a mobile manipulator which consists of an Omni-directional mobile base and a collaborative robotic arm. Next, its performance is compared with an admittance controller during a co-carry task of a partially deformable object in a 12-subjects user study. Quantitative and qualitative results of this experiment show that the proposed framework can effectively handle the transportation of objects regardless of their deformability and provides intuitive assistance to human partners. Finally, we have demonstrated the potential of our framework in a different scenario, where the human and the robot co-transport a manikin using a deformable sheet., 13 pages, 13 figures, submitted to IEEE Transactions on Automation Science and Engineering, for associated video, see https://youtu.be/oyfUkYj5WYw. arXiv admin note: substantial text overlap with arXiv:2201.10392

With the advent of Industry 4.0, new technologies are introduced to provide welfare beyond jobs and growth, such as collaborative robots. To be effective, the collaboration between human and robot should be safe, intuitive and stable. Safe collaboration does not only means avoiding human-robot contact, the operator must also feel confident during the collaboration with the robot and avoid incorrect postures (physical ergonomics). In this work, we analyzed an industrial use-case related to the automation of manual tasks in production processes. We propose a collaborative workcell design which integrates a solution to real-time remote control of operator's ergonomics in an interactive environment. The platform is tested in a real use case that involves several mechanical tasks in different scenarios, with and without the support of the robot. The results show both improvements in operator's working condition when supported by robots and overall in the efficiency of the process.

Recurrent or sustained awkward body postures are among the most frequently cited risk factors to the development of work-related musculoskeletal disorders (MSDs). To prevent workers from adopting harmful configurations but also to guide them toward more ergonomic ones, wearable haptic devices may be the ideal solution. In this paper, a vibrotactile unit, called ErgoTac, and a slide-and-squeeze unit, called CUFF, were evaluated in a limbs postural correction setting. Their capability of providing single-joint (shoulder or knee) and multi-joint (shoulder and knee at once) guidance was compared in twelve healthy subjects, using quantitative task-related metrics and subjective quantitative evaluation. An integrated environment was also built to ease communication and data sharing between the involved sensor and feedback systems. Results show good acceptability and intuitiveness for both devices. ErgoTac appeared as the suitable feedback device for the shoulder, while the CUFF may be the effective solution for the knee. This comparative study, although preliminary, was propaedeutic to the potential integration of the two devices for effective whole-body postural corrections, with the aim to develop a feedback and assistive apparatus to increase workers' awareness about risky working conditions and therefore to prevent MSDs., conference paper, 7 pages, 8 figures

Impacts and other non-smooth behaviors are usually unwanted in robotic applications. However, several industrial tasks such as deburring, removing excess material, and assembling/fitting, involve impacts between objects, which can benefit from robotic automation due to the risks posed to human health. Towards this objective, in this paper, we propose a method for optimal impact planning and pre-configuration for torque-controlled robots. We thus employ a well-known impulsive contact model to plan the impact force and create a hierarchical quadratic programming based controller capable of minimizing the robot’s peak torques by reconfiguring its joints optimally, before the impact occurs. The results obtained from multiple experiments during an industrial deburring task are discussed. Using a 7-DoF manipulator, we show consistent results, both in terms of accuracy of the impact force tracking with respect to the desired forces, and in terms of peak torques reduction and uniform torques distribution. &nbsp

In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body motions on a mobile collaborative robot. In order to validate our framework experimentally, we compared its performance with an admittance controller during a co-transportation task of a partially deformable object. We additionally demonstrate the potential of the framework while co-transporting rigid (aluminum rod) and highly deformable (rope) objects. A mobile manipulator which consists of an Omni-directional mobile base, a collaborative robotic arm, and a robotic hand is used as the robotic partner in the experiments. Quantitative and qualitative results of a 12-subjects experiment show that the proposed framework can effectively deal with objects of unknown deformability and provides intuitive assistance to human partners., Comment: 7 pages, 7 figures, accepted at IEEE International Conference on Intelligent Robots and Systems (IROS 2022), for associated video, see https://youtu.be/ounQmCdYlC4

Balance and gait disorders are the second leading cause of falls, which, along with consequent injuries, are reported as major public health problems all over the world. For patients who do not require mechanical support, vibrotactile feedback interfaces have proven to be a successful approach in restoring balance. Most of the existing strategies assess trunk or head tilt and velocity or plantar forces, and are limited to the analysis of stance. On the other hand, central to balance control is the need to maintain the body's centre of pressure (CoP) within feasible limits of the support polygon (SP), as in standing, or on track to a new SP, as in walking. Hence, this paper proposes an exploratory study to investigate whether vibrotactile feedback can be employed to lead human CoP during walking. The ErgoTac-Belt vibrotactile device is introduced to instruct the users about the direction to take, both in the antero-posterior and medio-lateral axes. An anticipatory strategy is adopted here, to give the users enough time to react to the stimuli. Experiments on ten healthy subjects demonstrated the promising capability of the proposed device to guide the users' CoP along a predefined reference path, with similar performance as the one achieved with visual feedback. Future developments will investigate our strategy and device in guiding the CoP of elderly or individuals with vestibular impairments, who may not be aware of or, able to figure out, a safe and ergonomic CoP path., 6 pages, 6 figures, accepted at IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). For associated video, see https://youtu.be/Hz42KohAgso

This paper proposes a novel Augmented Hierarchical Quadratic Programming (AHQP) framework for multi-tasking control in Human-Robot Collaboration (HRC) which integrates human-related parameters to optimize ergonomics. The aim is to combine parameters that are typical of both industrial applications (e.g. cycle times, productivity) and human comfort (e.g. ergonomics, preference), to identify an optimal trade-off. The augmentation aspect avoids the dependency from a fixed end-effector reference trajectory, which becomes part of the optimization variables and can be used to define a feasible workspace region in which physical interaction can occur. We then demonstrate that the integration of the proposed AHQP in HRC permits the addition of human ergonomics and preference. To achieve this, we develop a human ergonomics function based on the mapping of an ergonomics score, compatible with AHQP formulation. This allows to identify at control level the optimal Cartesian pose that satisfies the active objectives and constraints, that are now linked to human ergonomics. In addition, we build an adaptive compliance framework that integrates both aspects of human preferences and intentions, which are finally tested in several collaborative experiments using the redundant MOCA robot. Overall, we achieve improved human ergonomics and health conditions, aiming at the potential reduction of work-related musculoskeletal disorders.&nbsp

The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptability features. This paper proposes a model-based evolutionary optimization algorithm to tune the apparent impedance of a wrist rehabilitation device. We used passivity to define boundaries for the possible controller outcomes, limiting the shared autonomy of the robot and ensuring the coupled system stability. The experiment consists of a hardware-in-the-loop optimization and a one-degree-of-freedom robot used for wrist rehabilitation. Experimental results showed that the proposed technique could generate customized passive impedance controllers for three subjects. Furthermore, when compared with a constant impedance controller, the method suggested decreased in 20\% the root mean square of interaction torques while maintaining stability during optimization., Comment: 7 pages, 6 figures, submitted to IEEE International Conference on Robotics and Automation 2022

Over the past years, action recognition techniques have gained significant attention in computer vision and robotics research. Nevertheless, their performances in realistic applications, despite dedicated efforts to collect and annotate medium/large datasets, remain far from satisfactory, especially when it comes to applications in the field of human-robot collaboration. In response to this shortfall, we create a dataset not dispersive in its classes but sectoral, i.e., dedicated exclusively to the industrial environment and human-robot collaboration. Specifically, we describe our ongoing collection of the 'HRI30' database for industrial action recognition from videos, containing 30 categories of industrial-like actions and 2940 manually annotated clips. We test our dataset on multiple action detection approaches and compare it with the HMDB51 and UCF101 public datasets using the best-performing approach. We define a baseline of 86.55\% Top-1 accuracy and 99.76\% Top-5 accuracy, hoping that this dataset will encourage research towards understanding actions in collaborative industrial scenarios. The dataset can be downloaded at the following link: 10.5281/zenodo.5833411

This paper presents a mobile supernumerary robotic approach to physical assistance in human-robot conjoined actions. The study starts with a description of the SUPER-MAN concept. The idea is to develop and utilize mobile collaborative systems that can follow human loco-manipulation commands to perform industrial tasks through three main components: i) an admittance-type interface, ii) a human-robot interaction controller, and iii) a supernumerary robotic body. Next, we present two possible implementations within the framework from theoretical and hardware perspectives. The first system is called MOCA-MAN and comprises a redundant torque-controlled robotic arm and an omnidirectional mobile platform. The second one is called Kairos-MAN, formed by a high-payload 6-DoF velocity-controlled robotic arm and an omnidirectional mobile platform. The systems share the same admittance interface, through which user wrenches are translated to loco-manipulation commands generated by whole-body controllers of each system. Besides, a thorough user study with multiple and cross-gender subjects is presented to reveal the quantitative performance of the two systems in effort-demanding and dexterous tasks. Moreover, we provide qualitative results from the NASA-TLX questionnaire to demonstrate the SUPER-MAN approach's potential and its acceptability from the users' viewpoint., Comment: 16 pages, 14 figures. Associated video: https://youtu.be/_kfhLYQjhvA

This paper proposes a hybrid learning and optimization framework for mobile manipulators for complex and physically interactive tasks. The framework exploits an admittance-type physical interface to obtain intuitive and simplified human demonstrations and Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) to encode and generate the learned task requirements in terms of position, velocity, and force profiles. Next, using the desired trajectories and force profiles generated by GMM/GMR, the impedance parameters of a Cartesian impedance controller are optimized online through a Quadratic Program augmented with an energy tank to ensure the passivity of the controlled system. Two experiments are conducted to validate the framework, comparing our method with two approaches with constant stiffness (high and low). The results showed that the proposed method outperforms the other two cases in terms of trajectory tracking and generated interaction forces, even in the presence of disturbances such as unexpected end-effector collisions., Comment: 8 pages, 6 figures, accepted by IEEE Robotics and Automation Letters and IEEE International Conference on Intelligent Robots and Systems 2022

The use of assistive technologies in industrial environments to improve human ergonomics and comfort in repetitive and high effort tasks have increased considerably in the last decade. Predominantly, the goal is to provide additional physical support through lightweight and wearable devices, without posing major constraints to the human body movements. Towards achieving this objective, in this work we present a novel actuation mechanism for a soft assistive device, by taking into account the human elbow torque-angle profile. The proposed design integrates a single motor coupled with an elastic bungee and a cam-spool mechanism to enable energy exchange during the elbow flexion movement, while allowing for free-motions during the extension of the joint. A cable-driven transmission with passive elastic attachments is employed to implement compliant couplings with the wearer and to achieve easy donning/doffing. Experiments are conducted on two 3D printed functional prototypes. Results suggest that the assistive elbow torque is effectively transmitted with an average 90% success for balancing a 5N payload, and the free-motion range of 108 is measured for both flexion and extension.

The ongoing trend towards Industry 4.0 has revolutionised ordinary workplaces, profoundly changing the role played by humans in the production chain. Research on ergonomics in industrial settings mainly focuses on reducing the operator's physical fatigue and discomfort to improve throughput and avoid safety hazards. However, as the production complexity increases, the cognitive resources demand and mental workload could compromise the operator's performance and the efficiency of the shop floor workplace. State-of-the-art methods in cognitive science work offline and/or involve bulky equipment hardly deployable in industrial settings. This paper presents a novel method for online assessment of cognitive load in manufacturing, primarily assembly, by detecting patterns in human motion directly from the input images of a stereo camera. Head pose estimation and skeleton tracking are exploited to investigate the workers' attention and assess hyperactivity and unforeseen movements. Pilot experiments suggest that our factor assessment tool provides significant insights into workers' mental workload, even confirmed by correlations with physiological and performance measurements. According to data gathered in this study, a vision-based cognitive load assessment has the potential to be integrated into the development of mechatronic systems for improving cognitive ergonomics in manufacturing.

This work presents a unified approach for hybrid motion control of the MObile Collaborative Robotic Assistant (MOCA). The objective is to develop a loco-manipulation controller, enabling various couplings of the arm and the mobile base movements, and particularly their purely decoupled motions. The proposed method is based on a weighted whole-body Cartesian impedance controller, where the decoupling of the motions can be achieved by solving the local optimization problem of the weighted joint torques in the first task space and its nullspace, respectively. Under this control framework, by tuning the weighting terms and a nullspace gain, three motion modes, i.e. Locomotion, Manipulation, and Modified Loco-Manipulation, are implemented. To evaluate the proposed approach, a door opening task that requires different mobility patterns of the arm, the mobile base, and their coupled movements is demonstrated. The experiment results validate the proposed methodology and provide a comprehensive understanding of the differences among the above motion modes.

This paper presents a physical interface for collaborative mobile manipulators in industrial manufacturing and logistics applications. The proposed work builds on our earlier MOCA-MAN interface, through which an operator could be physically coupled to a mobile manipulator to be assisted in performing daily activities. The previous interface was based on a magnetic clamp attached to one arm of the user for the coupling stage, and a bracelet based on EMG sensors on the other arm for human-robot communication via gestures. The new interface instead presents the following additions: i) An industrial-like design that allows the worker to couple/decouple easily and to operate mobile manipulators locally; ii) A simplistic communication channel via a simple buttons board that allows controlling the robot with one hand only; iii) The interface offers enhanced loco-manipulation capabilities that do not compromise the worker mobility. In addition, an experimental evaluation with six human subjects is carried out to analyze the enhanced locomotion and flexibility of the proposed interface in terms of mobility constraint, usability, and physical load reduction., 7 pages, 6 figures, presented at IEEE International Conference on Robotics and Automation (ICRA 2022). Video available at: https://youtu.be/cDQK7Sy3p2Q

In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.

In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.

Even though collaborative robots could bring several advantages in industrial environments, still they do not populate adequately logistic and production processes. To enhance their adaptability to fast changes of product demand and, at the same time, ensure the safety of the human operator during the cooperation with cobots, we propose a strategy that can optimally regulate the effort distribution among the involved workers at the planning level during the performance of an assembly task. Such a method allocates an action to a worker only if such actions can be executed with low ergonomic risk, considering also the history of actions executed by the human. The allocation algorithm exploits a physical state estimation of the human operator based on kinematic measurements, which are updated during the cooperative task. To describe the action structure, we used an adapted version of AND/OR Graphs to handle also the role allocation problem. The preliminary experimental results show that the allocation changes during the cooperation, by assigning unsafe actions for the human worker to the robot.

Collaborative robots have demonstrated a high potential in addressing the flexibility needs of the increasingly competitive industry. They can simultaneously increase productivity, and reduce work related musculoskeletal disorders, which represent the single largest category of work-related disease in industrial countries. This can contribute to economic growth and creation of better, healthier, and more attractive working environments for the future workforce. This tutorial explains four fundamental aspects, i.e., technology, flexibility, interaction quality, and standardization, which are requited to create Human-Robot Collaboration (HRC) systems that can radically increase the flexibility and productivity of manufacturing while improving the ergonomics of the workplace.

This paper aims to improve the interaction and coordination between the human and the robot in cooperative execution of complex, powerful and dynamic tasks. We proposea novel approach that integrates online information about the human motor function and manipulability properties into the hybrid controller of the assistive robot. Through this human-in the-loop framework, the robot can adapt to the human motor behaviour and provide the appropriate assistive response in different phases of the cooperative task. We experimentally evaluate the proposed approach in two human-robot co-manipulations tasks that require specific complementary behaviour from the two agents. Results suggest that the proposed technique, which relies on a minimum degree of task-level pre-programming, can achieve an enhanced physical human-robot interaction performance and deliver appropriate level of assistance to the human operator.

This work addresses the magnetic-based control of a helical robot and the mitigation of the magnetic forces on its dipole moment during radial steering using rotating permanent magnets. A magnetic system with two synchronized permanent magnets that rotate quasistatically is used to move the helical robot (length and diameter of 12.5 mm and 4 mm, respectively). We experimentally demonstrate that using two synchronized permanent magnets for radial steering of a helical robot achieves higher motion stability, as opposed to propulsion using single rotating dipole field. The two synchronized dipole fields decrease the lateral oscillation (average peak-to-peak amplitude) of the helical robot by 37%, compared to the radial steering using a single dipole field at angular velocity of 31 rad/s. We also show that driving the helical robot using two synchronized rotating magnets achieves average swimming speed of 2.1 mm/s, whereas the single rotating dipole field achieves average swimming speed of 0.4 mm/s at angular velocity of 31 rad/s for the rotating permanent magnets. The proposed configuration of the helical propulsion allows us to decrease the magnetic forces that could cause tissue damage or potential trauma for in vivo applications.

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS) Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Collaborative robots have demonstrated a high potential in addressing the flexibility needs of the increasingly competitive industry. They can simultaneously increase productivity, and reduce work related musculoskeletal disorders, which represent the single largest category of work-related disease in industrial countries. This can contribute to economic growth and creation of better, healthier, and more attractive working environments for the future workforce. This tutorial explains four fundamental aspects, i.e., technology, flexibility, interaction quality, and standardization, which are requited to create Human-Robot Collaboration (HRC) systems that can radically increase the flexibility and productivity of manufacturing while improving the ergonomics of the workplace.

The paper describes the activities of the European project SOPHIA, Socio-Physical Interaction Skills for Cooperative Human-Robot Systems in Agile Production. The consortium involves European partners from academia, research organizations and industry. Themain goal of the project is to develop a newgeneration of CoBots andWearbots and advanced instrumental-based biomechanical risk assessment tools in industrial scenarios to reduce work-related musculoskeletal disorders and to improve productivity in industry 4.0. Further aimof the project is to create the basis for newergonomic international Standards for manual handling activities.

In this paper, we introduce an adaptive robotic manipulation framework to respond to the flexibility needs of common industrial tasks such as box-filling and item sorting. The proposed framework consists of a vision module and a robot control module. The vision module is responsible for the detection and tracking of the environment (e.g., box and the items), which is also capable of creating an occupancy grid in real-time, to continuously update the robot trajectory planner with the occupied portions of the detected box and their coordinates. The robot control module includes a trajectory planner and a self-tuning Cartesian impedance controller, to implement an adaptive strategy for the picking, placement, and sorting of the items in the box. The item-sorting strategy is based on our preliminary observations on human motor behavior, implementing a trade-off between the task execution accuracy and environmental perception uncertainty. The efficacy of the framework in performing a flexible box-filling task using a robot, autonomously or in collaboration with a human, is evaluated through several experiments.

Low-Back Pain (LBP) affects a large portion of the working population. Preventive exoskeletons have been proposed to reduce the moments on the lower back, specifically around the lumbosacral (L5/S1) joint. High correlation has been shown, between reducing the moments around the L5/S1 joint and intervertebral compression forces, which in turn have been identified as a risk factor for developing LBP. However, most passive back support exoskeletons use rigid plates or stiff beams to support the spine that limit the range of motion of the wearer. A large range of motion and versatility are especially desirable for industrial applications. To overcome these limitations, a passive biomimetic exo-spine has been designed, modelled and an initial prototype tested. Its potential to allow for a large range of motion, whilst at the same time limiting the most extreme and potentially harmful postures has been shown.

This paper presents design of a novel modular lower-limb gait exoskeleton built within the FP7 BioMot project. Exoskeleton employs a variable stiffness actuator (VSA) in all 6 joints, a directional-flexibility structure and a novel physical human-robot interfacing, which allow it to deliver the required output while minimally constraining user’s gait by providing passive degrees of freedom. Due to the modularity, exoskeleton can be used as a full lower-limb orthosis, a single-joint orthosis in any of the three joints, and a two-joint orthosis in a combi- nation of any of the two joints. By employing a simple torque control strategy, exoskeleton can be used to deliver a user- specific assistance, both in gait rehabilitation and in assisting people suffering musculoskeletal impairments. The result of the presented BioMot efforts is a low footprint-exoskeleton with powerful compliant actuators, simple, yet effective torque controller and easily adjustable flexible structure.

The human ankle joint plays a crucial role during walking. At the push-off phase the ankle plantarflexors generate the highest torque among the lower limb joints during this activity. The potential of the ankle plantarflexors is affected by numerous pathologies and injuries, which cause a decrease in the ability of the subject to achieve a natural gait pattern. Active orthoses have shown to have potential in assisting these subjects. The design of such robots is very challenging due to the contrasting design requirements of wearability (light weight and compact) and high torques capacity. This paper presents the development of a high-torque ankle actuator to assist the ankle joint in both dorsiflexion and plantarflexion. The compliant actuator is a spindle-driven MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator). The design of the actuator was made to keep its weight as low as possible, while being able to provide high torques. As a result of this novel design, the actuator weighs 1.18kg. Some static characterization tests were perfomed on the actuator and their results are shown in the paper.