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Tele-impedance augments classic teleoperation by enabling the human operator to actively command remote robot stiffness in real-time, which is an essential ability to successfully interact with the unstructured and unpredictable environment. However, the literature is missing a study on benefits and drawbacks of different types of stiffness command interfaces used in bilateral tele-impedance. In this article, we introduce a term called coupling effect, which pertains to the coupling between human-commanded stiffness going to the remote robot and force feedback coming from the remote robot. We hypothesize that, whenever the operator's commanded stiffness and force feedback are subject to coupling effect (e.g., muscle activity based stiffness command interfaces), force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. Although the coupling effect takes away some degree of the operator's control over the commanded stiffness, these involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. We examined the coupling effect in an experimental study with $\mathbf {16}$ participants, who performed position and force tracking tasks by using both coupled type (muscle activity based) and decoupled type (external device based) of interface. The results demonstrate a benefit of the coupling effect when the remote robot is operating in presence of unexpected force perturbations, where lower absolute error in position tracking task was observed. On the other hand, the decoupled type of interface is beneficial for force tracking tasks on the remote robot side, such as establishing or maintaining a stable contact with objects. However, the coupling effect negatively influences the commanding of reference stiffness to the remote robot in both position and force tracking tasks for the coupled type of interface, compared to the decoupled type of interface, which is not affected.

In this paper, we propose a method for selective monitoring and management of human muscle fatigue in human-robot co-manipulation scenarios. The proposed approach uses a machine learning technique to learn the complex relationship between individual human muscle forces, arm configuration and arm endpoint force that are provided by a sophisticated offline musculoskeletal model. The estimated muscle forces are used in the fatigue model to estimate the individual muscle fatigue levels online. Two fatigue management protocols are proposed that enable the robot to handle and reduce the human fatigue by altering the configuration of task execution. The first protocol uses optimisation technique to find the optimal position for task execution, where the fatigue-related endurance time can be maximised. The second protocol divides the arm muscles into groups and then alters the direction of endpoint force so that the fatigued muscle group can relax and the relaxed muscle group becomes active. The proposed method has a potential to enable the robot to facilitate safer and more ergonomic working conditions for the human coworker. The main advantage of this approach is that it can operate online, and that all the measurements can be performed by the robot sensory system, which can significantly increase the applicability in real world scenarios. To validate the proposed method, we performed multiple experiments with two collaborative tasks (polishing and drilling) under different conditions.

Ergonomics of human workers is one of the key elements in design and evaluation of production processes. Human ergonomics have a major impact on productivity as well as chronic health risks incurred by inappropriate working postures and conditions. In this paper we propose a novel method for estimating and communicating the ergonomic work condition called Binary Work-Condition Map, which provides a visualized feedback about work conditions of different configurations of an arm. The map is of binary nature and is derived by imposing the desired thresholds on considered ergonomic and safety related criteria. Therefore, the suggested arm postures in the map guarantee that all considered criteria are satisfied. This eliminates the ambiguity compared to state-of-the-art maps that uses continuous scales derived from weighted sum of multiple ergonomics criteria. In addition, to combine the advantages of both the binary map and the continuous map, we additionally propose a Hybrid Work-Condition Map that rules out unsuitable workspace with the binary map approach and renders the suitable workspace with the continuous map approach. The proposed approach was tested in simulation for various tasks and conditions. In addition, we conducted subjective evaluation experiments to compare the proposed methods with the state-of-the art method regarding the usability. The results indicated that the binary map is simpler to use, while the hybrid map is a good tradeoff between the binary and the continuous map. In selecting the map, strong points of each map should be considered with respect to the requirements of a specific application and task.

The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human–robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots.

In this paper, we propose a novel method for human---robot collaboration, where the robot physical behaviour is adapted online to the human motor fatigue. The robot starts as a follower and imitates the human. As the collaborative task is performed under the human lead, the robot gradually learns the parameters and trajectories related to the task execution. In the meantime, the robot monitors the human fatigue during the task production. When a predefined level of fatigue is indicated, the robot uses the learnt skill to take over physically demanding aspects of the task and lets the human recover some of the strength. The human remains present to perform aspects of collaborative task that the robot cannot fully take over and maintains the overall supervision. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed approach with experiments on real-world co-manipulation tasks: material sawing and surface polishing.

We propose a novel human-in-the-loop approach for teaching robots how to solve assembly tasks in unpredictable and unstructured environments. In the proposed method the human sensorimotor system is integrated into the robot control loop though a teleoperation setup. The approach combines a 3-DoF end-effector force feedback with an interface for modulation of the robot end-effector stiffness. When operating in unpredictable and unstructured environments, modulation of limb impedance is essential in terms of successful task execution, stability and safety. We developed a novel hand-held stiffness control interface that is controlled by the motion of the human finger. A teaching approach was then used to achieve autonomous robot operation. In the experiments, we analysed and solved two part-assembly tasks: sliding a bolt fitting inside a groove and driving a self-tapping screw into a material of unknown properties. We experimentally compared the proposed method to complementary robot learning methods and analysed the potential benefits of direct stiffness modulation in the force-feedback teleoperation.

In this paper, we introduce and explore a concept called coupling effect, which pertains to the influence of force feedback on the commanded stiffness that is voluntarily controlled by the operator through the stiffness interface during bilateral tele-impedance. The degree of coupling effect depends on the type of interface used to control the impedance of the remote robot. In case of muscle activity based stiffness command interfaces, the force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. These involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. To investigate the coupling effect in different types of stiffness command interfaces (i.e., coupled and decoupled), we conduct an experimental study in which participants are asked to perform position and force tracking tasks. The results show that in both position and force tracking tasks a lower tracking error of the reference stiffness is obtained with a decoupled interface (p

In this paper we propose a method for addressing the human operator's ergonomics during bilateral teleoperation. The method is based on human operator's musculoskeletal and fatigue models, whose input is the force produced by the operator through the haptic interface (master) in order to control the slave robot in the remote environment. The system then estimates the fatigue-based endurance time for producing the desired task force in different human arm configurations within the selected workspace. We perform an online optimisation process to find the optimal configuration that has the longest endurance time, which signifies that the operator can perform the task in that configuration for a longer period. Next, a trajectory is generated on the master robot in order to guide the human arm into the optimal configuration. The teleoperation is temporarily suspended by decoupling the master from the slave robot when the master robot is being reconfigured. The teleoperation will be resumed and the slave robot will be teleoperated again from where it stopped after the master robot guided the operator's arm to the optimised configuration. The main advantage of the proposed method is that the human operator can perform the task with less muscle fatigue, which increases the endurance time. To validate our approach, we performed proof-of-concept experiments on a teleoperation system composed of two Panda robots by Franka Emika, where one was serving as master and the other as slave. The main experimental task was for the human operator to produce a reference force with the slave robot on an object in a remote environment. A supplementary task involved putting an object of unknown mass on the slave robot end-effector.

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.

The deployment of industrial robotic cells based on lean manufacturing principles enables the development of fast-reconfigurable assembly lines in which human and robotic agents collaborate to achieve a shared task. To ensure the effective coordination of the shared effort, each task must be decomposed into a sequence of atomic actions that can be assigned either to a single agent or to the combination of more agents, according to a defined metric. While task allocation is a general problem and has been discussed intensively in other fields, less effort has been devoted in industrial scenarios, involving mixed human–robot teams and in particular, to the factors that should be considered in allocating tasks among a heterogeneous set of agents in collaborative manufacturing scenarios. In this letter, we investigate the agent characteristics that should be considered in the task allocation problem of fast-reconfigurable systems in industrial assembly processes. First, we introduce a set of indices, namely task complexity, agent dexterity, and agent effort, to evaluate agent performance with respect to a task. Second, we propose an offline allocation algorithm that combines the performance indices to assign optimally the task to the team agents. Finally, we validate the framework in a proof-of-concept collaborative assembly of a metallic structure. The results show that the workload is shared through the agents according to their particular physical capabilities and skill levels. A subjective analysis of the proposed collaborative framework on 12 healthy participants also validated the intuitiveness-of-use and improved performance.

This paper introduces a novel control framework for an arm exoskeleton that takes into account force of the human arm. In contrast to the conventional exoskeleton controllers where the assistance is provided without considering the human arm biomechanical force manipulability properties, we propose a control approach based on the arm muscular manipulability. The proposed control framework essentially reshapes the anisotropic force manipulability into the endpoint force manipulability that is invariant with respect to the direction in the entire workspace of the arm. This allows users of the exoskeleton to perform tasks effectively in the whole range of the workspace, even in areas that are normally unsuitable due to the low force manipulability of the human arm. We evaluated the proposed control framework with real robot experiments where subjects wearing an arm exoskeleton were asked to move a weight between several locations. The results show that the proposed control framework does not affect the normal movement behavior of the users while effectively reduces user effort in the area of low manipulability. Particularly, the proposed approach augments the human arm force manipulability to execute tasks equally well in the entire workspace of the arm.

Industry 4.0 envisions the integration of flexible and quickly reconfigurable robotic systems in assembly lines. This has led to the development of light-weight and adaptive collaborative robots with limited power and payload constraints. Hence, repetitive tasks or those that demand high forces may exceed such limits, resulting in robot damage and lost productivity. To address this problem, we propose a novel framework to prolong the lifetime of collaborative robots while guaranteeing the desired level of productivity. To address this, we propose a method that minimises a function of robot fatigue, an index able to model the robot motor usage at the joint level. This index features the desired external force, task duration, hardware parameters, and fatigue history. Moreover, future tasks are considered through fatigue thresholds imposed on specific joints, computed according to the robot safety requirements. We proved the effectiveness of the approach by comparing its results in terms of fatigue and torque with the well-known minimum effort approach. The results showed that our method ensures that the fatigue thresholds are not exceeded.

Collaborative robots are often designed with limited power and force capacity, with the aim to provide affordable solutions and ensure human safety in case of accidental collisions and impacts. If a task requires a power beyond this capacity, or is performed repeatedly over long periods, such limits may be exceeded, which can cause inevitable robot damage and contribute to the lost productivity. In such cases, where hardware solutions and improvements are not applicable, effective software frameworks can prolong robot productivity and lifetime. To this end, in this paper we propose a novel technique for the monitoring and management of robot fatigue in repetitive or high-effort task execution scenarios. The robot fatigue is estimated by the measured temperature of motors in the joints. The proposed fatigue management system is composed of two-stage reaction process that is triggered by different levels of the estimated fatigue. The first stage exploits the kinematic redundancy of robot structure in attempt to minimise the load in the specific joints that under fatigue by reconfiguration in the joint space through the null space of the Cartesian task production. If the first stage is not successful in reducing the fatigue, the second stage is activated that gradually reduces the forces of hybrid controller. At that point, the human co-worker can temporarily take over the task execution until the robot will be recovered from the excessive fatigue. To validate the proposed approach we conducted experiments on KUKA Lightweight Robot performing two interaction tasks: autonomous surface wiping and collaborative human-robot surface polishing.

In this paper, we propose a novel method for selective management of muscle fatigue in human-robot co-manipulation. The proposed framework enables the detection of excessive fatigue levels of an individual muscle group while executing a certain task, and provides anticipatory robotic responses to distribute the effort among less-fatigued muscles of human arm. Our approach uses a machine learning technique to enable online predictions of muscle forces in different arm configurations and endpoint interaction forces. The estimated muscle forces are then used for the model-based estimation of muscle fatigue levels. Through optimisation, the fatigue management system can alter the task execution in a way that specific fatigued muscles are offloaded, while at the same time enables the production of task force using muscles with lower levels of fatigue. The main advantage of the proposed method is that it can operate online, and that all the measurements are performed by the robot sensory system, which can significantly increase the applicability in real-world scenarios. To validate the proposed method, we performed proof-of-concept experiments where the task of the human operator was to use a tool to polish an object that was manipulated by the robot.

Two basic trade-offs interact while our brain decides how to move our body. First, with the cost-benefit trade-off, the brain trades between the importance of moving faster toward a target that is more rewarding and the increased muscular cost resulting from a faster movement. Second, with the speed-accuracy trade-off, the brain trades between how accurate the movement needs to be and the time it takes to achieve such accuracy. So far, these two trade-offs have been well studied in isolation, despite their obvious interdependence. To overcome this limitation, we propose a new model that is able to simultaneously account for both trade-offs. The model assumes that the central nervous system maximizes the expected utility resulting from the potential reward and the cost over the repetition of many movements, taking into account the probability to miss the target. The resulting model is able to account for both the speed-accuracy and the cost-benefit trade-offs. To validate the proposed hypothesis, we confront the properties of the computational model to data from an experimental study where subjects have to reach for targets by performing arm movements in a horizontal plane. The results qualitatively show that the proposed model successfully accounts for both cost-benefit and speed-accuracy trade-offs.

In this paper, we propose a novel method for the control of human-robot eo-manipulation that takes into aeeount the ergonomie requirements for the human eo-worker. The robot uses a whole-body dynamic model of the human to optimise for the position of the eo-manipulation task in the workspace. In this configuration, the overloading joint torques, i.e. the effects of an external load in human body joints, are minimised. In addition, the optimisation process ineludes several constraints, sueh as human arm manipulability properties, to ensure that the human has a good manipulation eapaeity in the given eonfiguration. The main advantage of this approaeh is that the robot ean potentially help to reduee the work-related strain and increase the productivity of the human eo-worker. In addition, the on-line estimation of the overloading joint torques does not require an external foree plate or sensor insoles. We validated the proposed method with experiments in two eo-manipulation tasks: human using a device to polish an object that is delivered by the robot and a human-robot object handover. The results show that the proposed method is successful in achieving the ergonomie conditions for the human during the eollaboration.

In this paper we study the concept of robots learning from collaboration with skilled robots. The advantage of this concept is that the human involvement is reduced, while the skill can be propagated faster among the robots performing similar collaborative tasks or the ones being executed in hostile environments. The expert robot initially obtains the skill through the observation of, and physical collaboration with the human. We present a novel approach to how a novice robot can learn the specifics of the co-manipulation task from the physical interaction with an expert robot. The method consists of a multi-stage learning process that can gradually learn the appropriate motion and impedance behaviour under given task conditions. The trajectories are encoded with Dynamical Movement Primitives and learnt by Locally Weighted Regression, while their phase is estimated by adaptive oscillators. The learnt trajectories are replicated by a hybrid force/impedance controller. To validate the proposed approach we performed experiments on two robots learning and executing a challenging co-manipulation task.

International audience; Two basic trade-offs interact while our brain decides how to move our body. First, with the cost-benefit trade-off, the brain trades between the importance of moving faster toward a target that is more rewarding and the increased muscular cost resulting from a faster movement. Second, with the speed-accuracy trade-off, the brain trades between how accurate the movement needs to be and the time it takes to achieve such accuracy. So far, these two trade-offs have been well studied in isolation, despite their obvious interdependence. To overcome this limitation, we propose a new model that is able to simultaneously account for both trade-offs. The model assumes that the central nervous system maximizes the expected utility resulting from the potential reward and the cost over the repetition of many movements, taking into account the probability to miss the target. The resulting model is able to account for both the speed-accuracy and the cost-benefit trade-offs. To validate the proposed hypothesis, we confront the properties of the computational model to data from an experimental study where subjects have to reach for targets by performing arm movements in a horizontal plane. The results qualitatively show that the proposed model successfully accounts for both cost-benefit and speed-accuracy trade-offs.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. We propose an approach to efficiently teach robots how to perform dynamic anipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person crosscut saw. The challenge of this experiment is that it requires precise coordination of the robot’s motion and complianceaccording to the partner’s actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner’s motion. Slovenian Research Agency ; Slovenian Ministry of Higher Education, Science and Technology.

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

In this paper, we propose and evaluate methods for the local navigation using only visual perception for the skiing robot. Our skiing robot, capable of skiing using the carving technique, has no direct control on the velocity of skiing as it cannot break or accelerate, therefore well known navigation methods for nonholonomic mobile robots cannot be directly applied. We consider the following methods: an intuitive method of aiming at the closest gates, a human obstacle avoidance movement model, neural networks learning from a set of human demonstrations, and a global method that uses a predefined, spline-encoded path. The navigation performance of the robot on unknown ski courses is evaluated using two criteria: successful completion of the course and the time required to complete the course. Simulation results show the applicability and drawbacks of presented methods. Finally, the method using the neural networks was applied on a real-world skiing robot and we tested navigating a slalom course on both roller blades and skies.

Musculoskeletal disorders, the single largest category of workrelated injuries in many industrial countries, are associated with very high costs in terms of lost productivity. In highvolume production facilities, large parts of the workstation should ideally be adapted to individual workers in real time to prevent such injuries. However, in smaller production lines, especially those found in small and medium enterprises (SMEs), regularly adapting the entire workstation to accommodate flexibility is a major challenge. A solution to the problem is the development of reconfigurable human-robot collaboration (HRC) workstations and frameworks. These are key to enabling agile manufacturing, by merging the dexterity, flexibility, and problem-solving ability of humans with the strength and precision of robotics.

In this paper, we propose a method that allows the robot to adapt its physical behaviour to the human fatigue in human-robot co-manipulation tasks. The robot initially imitates the human to perform the collaborative task in a leader-follower setting, using a feedback about the human motor behaviour. Simultaneously, the robot obtains the skill in online manner. When a predetermined level of human fatigue is detected, the robot uses the learnt skill to take over the physically demanding aspect of the task and contributes to a significant reduction of the human effort. The human, on the other hand, controls and supervises the high-level interaction behaviour and performs the aspects that require the contribution of both agents in such a dynamic co-manipulation setup. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed method with experiments on a real-world co-manipulation task with environmental constraints and dynamic uncertainties.