Wearable assistive devices that monitor muscle fatigue reduce the risk of work-related musculoskeletal disorders, enhance rehabilitation outcomes, and extend operational time by optimizing the power consumption of the device. This work proposes a muscle fatigue-aware controller (MFAC) for a semi-rigid knee exoskeleton. During an offline calibration phase, we use Gaussian Process Regression (GPR) to model the relationship between muscle activation (measured via EMG) and the corresponding joint moment and angle, enabling fatigue state estimation for the controller. The trained model then approximates muscle activation online using only joint states and moment derived from user’s kinematic data and ground reaction forces provided by the wearable device. The estimated muscle activation is used to assess the muscle fatigue state through a model-based fatigue evaluation module. Notably, EMG measurement is only required during the offline training in our approach, enabling EMG-free online estimation, which significantly enhances the feasibility for long-term mobile applications. Building on muscle fatigue and human-exoskeleton interaction models, we then developed an adaptive controller within a predictive control framework. The resulting optimization problem generates control signals that adjust assistance to reduce the fatigue progression. Two experiments validate the EMG-free fatigue estimation method and the integrated MFAC, demonstrating accurate muscle activation estimation and effective adaptive assistance based on the estimated fatigue state. Analysis of actuator power output reveals adaptivity in which the controller conserves energy during low muscle fatigue and progressively improves power output with increasing fatigue, suggesting a longer duration of the device with a fixed battery capacity.

Human–Robot Interaction (HRI) has emerged as a pivotal domain in robotics, centering on the interplay and collaboration between humans and robots to achieve complex tasks. Effective communication is a cornerstone of successful HRI, facilitating the exchange of critical information essential for joint decision-making and task execution. This article explores the intricate dynamics of collaborative communication in physical HRI (pHRI), specifically focusing on non-verbal cues. Within HRI, we assert that collaboration fundamentally hinges on communication, wherein agents share information to achieve common objectives. Information theory provides a rigorous mathematical framework for quantifying the flow of information within communicating agents. It serves as a unifying framework for evaluating the dynamic interplay of various communication channels in pHRI. This study introduces a non-parametric approach based on information entropy to assess communication between agents in pHRI scenarios and detect important behaviors, such as information flow, leadership, and coupling. Through a comprehensive experimental setup involving collaborative catching tasks, we demonstrate the versatility and applicability of the proposed methodology.

Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. To address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals. The prototype, constructed around a wrist brace, functions as a supernumerary finger placed near the index, allowing for early-stage evaluation on unimpaired subjects. A neural network-based model was then implemented to estimate fingertip forces from EMG inputs, allowing for online adjustment of the prosthetic finger grip strength. The force estimation model was validated through experiments with ten participants, demonstrating its effectiveness in predicting forces. Additionally, online trials with four users wearing the prosthesis exhibited precise control over the device. Our findings highlight the potential of using EMG-based force estimation to enhance the functionality of prosthetic fingers.

In physical human-robot interaction, force feedback has been the most common sensing modality to convey the human intention to the robot. It is widely used in admittance control to allow the human to direct the robot. However, it cannot be used in scenarios where direct force feedback is not available since manipulated objects are not always equipped with a force sensor. In this work, we study one such scenario: the collaborative pushing and pulling of heavy objects on frictional surfaces, a prevalent task in industrial settings. When humans do it, they communicate through verbal and non-verbal cues, where body poses, and movements often convey more than words. We propose a novel context-aware approach using Directed Graph Neural Networks to analyze spatiotemporal human posture data to predict human motion intention for non-verbal collaborative physical manipulation. Our experiments demonstrate that robot assistance significantly reduces human effort and improves task efficiency. The results indicate that incorporating posture-based context recognition, either together with or as an alternative to force sensing, enhances robot decision-making and control efficiency.

Robotic manipulation in dynamic environments often requires seamless transitions between different grasp types to maintain stability and efficiency. However, achieving smooth and adaptive grasp transitions remains a challenge, particularly when dealing with external forces and complex motion constraints. Existing grasp transition strategies often fail to account for varying external forces and do not optimize motion performance effectively. In this work, we propose an Imitation-Guided Bimanual Planning Framework that integrates efficient grasp transition strategies and motion performance optimization to enhance stability and dexterity in robotic manipulation. Our approach introduces Strategies for Sampling Stable Intersections in Grasp Manifolds for seamless transitions between uni-manual and bi-manual grasps, reducing computational costs and regrasping inefficiencies. Additionally, a Hierarchical Dual-Stage Motion Architecture combines an Imitation Learning-based Global Path Generator with a Quadratic Programming-driven Local Planner to ensure real-time motion feasibility, obstacle avoidance, and superior manipulability. The proposed method is evaluated through a series of force-intensive tasks, demonstrating significant improvements in grasp transition efficiency and motion performance. A video demonstrating our simulation results can be viewed at https://youtu.be/3DhbUsv4eDo.

Accurate and physically feasible human motion prediction is crucial for safe and seamless human-robot collaboration. While recent advancements in human motion capture enable real-time pose estimation, the practical value of many existing approaches is limited by the lack of future predictions and consideration of physical constraints. Conventional motion prediction schemes rely heavily on past poses, which are not always available in real-world scenarios. To address these limitations, we present a physics-informed learning framework that integrates domain knowledge into both training and inference to predict human motion using inertial measurements from only 5 IMUs. We propose a network that accounts for the spatial characteristics of human movements. During training, we incorporate forward and differential kinematics functions as additional loss components to regularize the learned joint predictions. At the inference stage, we refine the prediction from the previous iteration to update a joint state buffer, which is used as extra inputs to the network. Experimental results demonstrate that our approach achieves high accuracy, smooth transitions between motions, and generalizes well to unseen subjects. The source code and data are available at https://github.com/ami–iit/paper_guo_2025_iros_human_kinematics_prediction.

Object re-identification and tracking lay the foundation for various computer vision and robotics applications. In this study, we propose a method for personalizing a neural network to enhance and continuously adapt the re-identification of a specific target. Employing an unsupervised continual learning approach in conjunction with an intelligent image pool collection, we can effectively track the target and mitigate the issue of catastrophic forgetting, a challenge prevalent in this research domain. Our primary goal is to provide a robust person re-identification approach to extend the capabilities of recent tracking frameworks employed in robotics, which we have adopted as our baselines for evaluation. Our results demonstrate our approach’s efficacy in successfully re-identifying the target, even when the target drastically changes his clothing appearance and the baseline frameworks struggle. To optimally tune the framework parameters, we conducted an ablation study and substantiated our findings with saliency maps to elucidate the reasons behind the effectiveness of our approach.

Detecting and preventing falls in humans is a critical component of assistive robotic systems. While significant progress has been made in detecting falls, the prediction of falls before they happen, and analysis of the transient state between stability and an impending fall remain unexplored. In this paper, we propose a anticipatory fall detection method that utilizes a hybrid model combining Dynamic Graph Neural Networks (DGNN) with Long Short-Term Memory (LSTM) networks that decoupled the motion prediction and gait classification tasks to anticipate falls with high accuracy. Our approach employs real-time skeletal features extracted from video sequences as input for the proposed model. The DGNN acts as a classifier, distinguishing between three gait states: stable, transient, and fall. The LSTM-based network then predicts human movement in subsequent time steps, enabling early detection of falls. The proposed model was trained and validated using the OUMVLP-Pose and URFD datasets, demonstrating superior performance in terms of prediction error and recognition accuracy compared to models relying solely on DGNN and models from literature. The results indicate that decoupling prediction and classification improves performance compared to addressing the unified problem using only the DGNN. Furthermore, our method allows for the monitoring of the transient state, offering valuable insights that could enhance the functionality of advanced assistance systems.

Multi-agent collaborative simultaneous localization and mapping (Mac-SLAM) facilitates mutual localization among multi-agent and mapping in unknown environments. However, Mac-SLAM faces two main practical challenges in resource-constrained situations: heavy communication load and conflicts among multi-source maps. To address these issues, we propose Alice-SLAM: an accurate and lite-communication client-server collaborative SLAM system, reducing communication load while accuracy-guaranteed. Specifically, regarding high communication demand, we optimize communication load by compressing keyframe data and sharing only key map information instead of full map information. For inconsistency among multi-maps, we combine specific bundle adjustments (BA) and an adaptive strategy for active map optimization to enhance the consistency of the global map. A set of experiments demonstrates the superior accuracy and reduced communication load of the proposed Alice-SLAM on the EuRoC dataset and in multi-user augmented reality (AR) experiments conducted in our lab, highlighting its effectiveness in resource-constrained cases. We plan to open-source our code1to encourage further research and collaboration in this area.

In this paper, we propose a motion, stiffness, and force learning framework based on an extended dynamic movement primitive (DMP) and quadratic programming (QP) optimization. The objective is to learn kinematic and dynamic operational parameters from a one-shot human demonstration, through measurement and estimation of the motion, 3-dimensional (3-D) endpoint stiffness, and applied forces of the human arm during manipulation tasks. To this end, first, the framework features an extended DMP to model the motion, stiffness, and force variations in Cartesian space and 2-D sphere manifold. Second, to account for collected errors and human-robot operation gaps, a QP optimization is applied to fine-tune the desired position of the controller. Finally, we validate the framework through two experiments in real scenarios on the Franka Emika Panda robot. Experimental results show that the robot can not only inherit the variation laws of motion, stiffness, and force in the human demonstration, but also exhibit certain generalization capabilities to other situations. The framework provides a reference for robots learning multiple skills via a one-shot human demonstration, which finds great potential application in human-robot cooperation, contact-rich scenarios, and skillful operations, where the motion, stiffness, and applied forces need to be considered simultaneously. Note to Practitioners—Fast programming in robotics through skill transfer plays a critical role in next-generation robots entering ordinary people’s lives. Existing research focuses more on skill learning at the kinematic level and lacks on the dynamic level, such as stiffness and contact force. The goal of this paper is to propose a novel framework for robots learning of motion, stiffness, and force variations from a one-shot human demonstration, simultaneously. To this end, a Riemannian-based DMP method is employed to model the variation laws of motion, stiffness, and force in Cartesian space and 2-D sphere manifold, respectively. In this way, the learning module needs to be run only once, and the patterns can also be generalized to other targets without repeated robot teaching and additional time-consuming processes. To accurately reproduce the learned skills, a human-like motion/stiffness/force controller combined with QP optimization is investigated. In this paper, rather than identifying real environmental parameters, we directly use interacted forces during the human demonstration to represent environmental effects and employ QP to update the desired position in a limited range to account for collected errors and human-robot operation gaps. Experiments on button pressing and polishing tasks by the Panda robot have achieved very good results. The work of this paper lays a foundation for multiple skills learning from human demonstration (LfHD).

Visual Simultaneous Localization and Mapping (vSLAM) is a cornerstone technology in computer vision and robotics, underpinning applications such as autonomous vehicles and robot navigation. While traditional vSLAM systems have shown significant progress in indoor or outdoor environments, their performance often degrades in complex scenes, limiting their adaptability and robustness. Semantic vSLAM, which integrates high-level semantic information into vSLAM systems, has emerged as a promising solution to address these limitations by enabling a richer understanding of the environment. In this paper, we provide a comprehensive review of semantic vSLAM, offering a critical analysis of its evolution, methods, and challenges. We begin by revisiting the development of traditional vSLAM, emphasizing its limitations and the motivation for incorporating semantic information. Subsequently, we delve into the core modules of semantic vSLAM, including semantic extraction, object association, semantic loop closing, back-end optimization, and semantic mapping. Then, we present a performance comparison of semantic vSLAM systems under two different datasets, indoor and outdoor, respectively. Furthermore, we also provide a comparative analysis of widely used SLAM datasets to provide guidance for performance testing and validation. To further enrich the discussion, we identify unresolved challenges in semantic vSLAM, such as long-term semantic perception and association, open and unstructured environments. We propose future research directions, including balancing computational resources and quantifying system risk, large model-based navigation and mapping, and embodied AI SLAM. By providing key insights and forward-looking perspectives, this work aims to stimulate future research and improve the capabilities of semantic vSLAM in real-world applications.

Despite impressive advancements of industrial collaborative robots, their potential remains largely untapped due to the difficulty in balancing human safety and comfort with fast production constraints. To help address this challenge, we present PRO-MIND, a novel human-in-the-loop framework that exploits valuable data about the human coworker to optimize robot trajectories. By estimating human attention and mental effort, our method dynamically adjusts safety zones and enables on-the-fly alterations of the robot path to enhance human comfort and optimal stopping conditions. Moreover, we formulate a multiobjective optimization to adapt the robot's trajectory execution time and smoothness based on the current human psychophysical stress, estimated from heart rate variability and frantic movements. These adaptations exploit the properties of B-spline curves to preserve continuity and smoothness, which are crucial factors in improving motion predictability and comfort. Evaluation in two realistic case studies showcases the framework's ability to restrain the operators' workload and stress and to ensure their safety while enhancing human–robot productivity. Further strengths of PRO-MIND include its adaptability to each individual's specific needs and sensitivity to variations in attention, mental effort, and stress during task execution.

Observational learning is a promising approach to enable people without expertise in programming to transfer skills to robots in a user-friendly manner, since it mirrors how humans learn new behaviors by observing others. Many existing methods focus on instructing robots to mimic human trajectories, but motion-level strategies often pose challenges in skills generalization across diverse environments. This article proposes a novel framework that allows robots to achieve ahigher-levelunderstanding of human-demonstrated manual tasks recorded in RGB videos. By recognizing the task structure and goals, robots generalize what observed to unseen scenarios. We found our task representation on Shannon's Information Theory (IT), which is applied for the first time to manual tasks. IT helps extract the active scene elements and quantify the information shared between hands and objects. We exploit scene graph properties to encode the extracted interaction features in a compact structure and segment the demonstration into blocks, streamlining the generation of behavior trees for robot replicas. Experiments validated the effectiveness of IT to automatically generate robot execution plans from a single human demonstration. In addition, we provide HANDSOME, an open-source dataset of HAND Skills demOnstrated by Multi-subjEcts, to promote further research and evaluation in this field.

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario.

Preserving and encouraging mobility in the elderly and adults with chronic conditions is of paramount importance. However, existing walking aids are either inadequate to provide sufficient support to users’ stability or too bulky and poorly maneuverable to be used outside hospital environments. In addition, they all lack adaptability to individual requirements. To address these challenges, this paper introduces WANDER, a novel Walking Assistive omNi-Directional Exo-Robot. It consists of an omnidirectional platform and a robust aluminum structure mounted on top of it, which provides partial body weight support. A comfortable and minimally restrictive coupling interface embedded with a force/torque sensor allows to detect users’ intentions, which are translated into command velocities by means of a variable admittance controller. An optimization technique based on users’ preferences, i.e., Preference-Based Optimization (PBO) guides the choice of the admittance parameters (i.e., virtual mass and damping) to better fit subject-specific needs and characteristics. Experiments with twelve healthy subjects exhibited a significant decrease in energy consumption and jerk when using WANDER with PBO parameters as well as improved user performance and comfort. The great interpersonal variability in the optimized parameters highlights the importance of personalized control settings when walking with an assistive device, aiming to enhance users’ comfort and mobility while ensuring reliable physical support.

An emerging trend in the field of human-robot collaboration is the disassembly of end-of-life products. Safety is a crucial requirement of the disassembly process since worn-out or damaged products could break, possibly resulting in dangerous behavior of the robot. To protect the user from such behavior, this work addresses this challenge through the implementation of an energy-aware Cartesian impedance controller, combined with virtual workspace restrictions. Hereby, the passivity of the robotic system is ensured. The paper proposed two approaches to ensure the passivity of the system when subjected to workspace restrictions due to unplanned interactions and contact loss. The first approach employs an augmented energy tank with restricted energy flow. The second approach monitors the overall energy flow, regulating and separating non-passive behavior, caused by workspace restrictions. The approaches are evaluated and compared with each other, by using a KUKA LBR iiwa robot. The results highlight the potential of virtual workspace restrictions in human-robot collaborative disassembly tasks.

In scenarios of Human-Robot Interaction (HRI), it is often assumed that the robot should cooperate with the closest individual or that only one person is present. However, in real-life situations, such as shop floor operations, this assumption may not hold. Thus, it becomes necessary for a robot to recognize a specific target in a crowded environment. To address this problem, we propose a person re-identification module that uses continuous visual adaptation techniques. This module ensures that the robot can seamlessly cooperate with the appropriate individual despite its appearance changes or partial or total occlusions. We used both a laboratory environment and an HRI scenario where the robot followed a person to test our framework. During the test, the targets were asked to change their appearance and disappear from the camera’s field of view to test the module’s ability to handle challenging cases of occlusion and outfit variations. We compared our framework with a state-of-the-art Multi-Object Tracking (MOT) method, and the results showed that our module, shortly named CARPE-ID, accurately tracked each selected target throughout the experiments in all cases except for two cases. In contrast, the MOT had an average of 4 tracking errors for each video.

To enhance robots’ applicability in real-world scenarios, it is essential to establish a complex and multi-tasking behaviour, inspired by human nature. To this purpose, from a hardware perspective, a high number of degrees of freedom is necessary, as is the case for humanoids and collaborative mobile manipulators. From a software standpoint instead, complex hierarchical strategies are often used to define a set of behaviours that the robot should reflect in strict hierarchical order. Their main issue however, is related to the lack of continuity when their stack of tasks is changed. Existing works that address this issue clearly present a trade-off between optimality assurance during transition and computational costs. Here, we employ a distributed processing approach that enables not only the minimization of computational costs, but also continuous optimality and constraints feasibility even under sharp transitions. The approach is tested during three task transitions, for different tasks such as constrained trajectory tracking, obstacle avoidance, and postural optimization. Two mobile manipulators are used, each having 10 DoF, and the results confirm the smoothness of the generated solutions.

To enable a comprehensive human-robot interaction, it is essential to refer to human-human collaboration and decode complex non-verbal communication aspects that are essential for adaptive decision-making and task success. Indeed, for a robust collaboration, it is useful to understand the intricacies and complexities of human communication during human-human interaction and to compare it with the human-robot interaction case. We study this communication exchange and information flow by evaluating the leader/follower behavior during physical interaction with different agents and different control types, focusing on non-verbal cues, to identify collaborative or competitive attitudes. To achieve this, we consider a dynamic task of collaboratively catching a falling object, which, by its nature, favors non-verbal communication channels. Multiple subjects performed the same task with different collaborative agents (i.e., human and robot) and with different control modalities, to evaluate the leadership roles and their implication on task success (successfully catching the object while minimizing impact forces). We analyze how the impact force minimization induced by the velocity matching optimal planner affects the catching success rate. The information flow is analyzed, and the leadership roles are identified. Further qualitative data is gathered from questionnaires and compared with respect to the analytic results.

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.Note to Practitioners—Transportation of objects which requires the cooperation of multiple partners, is a common task in industrial settings such as factories and warehouses. The existing human-robot collaboration solutions for this task have focused only on purely rigid objects, although deformable objects need to be carried frequently in real-world applications. In this paper, we introduce a human-robot collaborative transportation framework that can handle objects with different deformability ranging from purely rigid to highly deformable. In particular, the proposed framework generates whole-body movements on a mobile collaborative robot by combining of the haptic information transmitted through the object and the human motion information obtained from a motion capture system. Moreover, the framework includes an intuitive way to rotate the object during the execution based on human hand and torso motion. The results of the experiments where objects with various deformability characteristics were transported in collaboration with a mobile manipulator demonstrated the high potential of the proposed approach in a laboratory setting. In the future, we plan to employ a less expensive vision-based human motion tracking system instead of the IMU-based system used in this study. With this change, we will be able to eliminate the need for wearable sensors from the framework presented, which would enhance its usability in real-world scenarios.

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.

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.

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.

This paper proposes a combined 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.

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.

Despite the significant progress made in making robots more intelligent and autonomous, today, teleoperation remains a dominant robot control paradigm for the execution of complex and highly unpredictable tasks. Attempts have been made to make teleoperation systems stable, easy to use, and efficient in terms of physical interactions between the follower remote robot and the environment. In particular, the emergence of torque-controlled robots has permitted to regulate the interaction forces from a distance through direct force or impedance control, enabling them to engage in complex interaction tasks. Exploiting this feature, the concept of teleimpedance control was introduced as an alternative method to bilateral force-reflecting teleoperation. The aim was to create a feed-froward yet contact-efficient teleoperation by enriching the leader commands with desired impedance profiles while executing a task. Since then, the teleimpedance concept has found its way into a wide range of interface and controller designs, as well as application domains. Accordingly, after a decade of research progress, this survey aims to provide: first, a convenient introduction of the concept to new researchers in the field, second, consolidate the existing state-of-the-art for active researchers, third, and discuss the pros and cons of different methods in terms of interface and force feedback to provide guidelines for different applications and future developments.

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 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.

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.

Even though cobots have high potential in bringing several benefits in the manufacturing and logistic processes, their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a novel method that optimizes assembly strategies and distributes the effort among the workers in human-robot cooperative tasks. The cooperation model exploits AND/OR Graphs that we adapted to solve also the role allocation problem. The allocation algorithm considers quantitative measurements that are computed online to describe human operators' ergonomic status and task properties. We conducted preliminary experiments to demonstrate that the proposed approach succeeds in controlling the task allocation process to ensure safe and ergonomic conditions for the human worker.

This paper presents the design and development of a robotic system to give physical assistance to the elderly or people with neurological disorders such as Ataxia or Parkin-son's. In particular, we propose using a mobile collaborative robot with an interaction-assistive whole-body interface to help people unable to maintain balance. The robotic system consists of an Omni-directional mobile base, a high-payload robotic arm, and an admittance-type interface acting as a support handle while measuring human-sourced interaction forces. The postural balance of the human body is estimated through the projection of the body Center of Mass (CoM) to the support polygon (SP) representing the quasi-static Center of Pressure (CoP). In response to the interaction forces and the tracking of the human posture, the robot can create assistive forces to restore balance in case of its loss. Otherwise, during normal stance or walking, it will follow the user with minimum/no opposing forces through the generation of coupled arm and base movements. As the balance-restoring strategy, we propose two strategies and evaluate them in a laboratory setting on healthy human participants. Quantitative and qualitative results of a 12-subjects experiment are then illustrated and discussed, comparing the performances of the two strategies and the overall system.

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.

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the operator's cognitive workload and stress. The method exploits the generation of B-spline trajectories in the joint space and formulation of a multi-objective optimisation problem to online adjust the total execution time and smoothness of the robot trajectories. The former ensures human efficiency and productivity of the workplace, while the latter contributes to safeguarding the user's comfort and cognitive ergonomics. The performance of the proposed framework was evaluated in a typical industrial task. Results demonstrated its capability to enhance the productivity of the human-robot dyad while mitigating the cognitive workload induced in the worker.

Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.

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.

The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision techniques to perform human action recognition and image classification, which are integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme to hierarchically optimize the robot's reactive behavior and human ergonomics. The proposed framework allows one to intuitively command the robot in space while a task is being executed. The experiments confirm increased human ergonomics and usability, which are fundamental parameters for reducing musculoskeletal diseases and increasing trust in automation.

Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.

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.

This paper proposes a novel framework for robotic manipulation tasks, exploiting the Human-Robot Collaboration (HRC) potential. The framework integrates two adaptive controllers to i) modulate robot compliance in contact with the environment along constrained directions, and to ii) enable human guidance through touch when a manual intervention is needed. To demonstrate the potential of the proposed frame-work, we consider a collaborative screwing task. In this example application, the operator is in charge of placing the screws on the table and following the instructions on a graphical user interface. The robot, after identifying the position of the screws through an online human pose-tracking system, performs the screwing using the proposed controller. The human operator can adjust the screwing position of the robot using the adaptive interface at anytime if the position accuracy through vision is insufficient. We first experimentally evaluate the operation of the proposed controller and demonstrate its performance in comparison to the classical impedance control. Next, the overall system is evaluated in a collaborative (human and robot) setting.

Human factors and ergonomics are the essential constituents of teleoperation interfaces, which can significantly affect the human operator’s performance. Thus, a quantitative evaluation of these elements and the ability to establish reliable comparison bases for different teleoperation interfaces are the keys to select the most suitable one for a particular application. However, most of the works on teleoperation have so far focused on the stability analysis and the transparency improvement of these systems and do not cover the important usability aspects. In this article, we propose a foundation to build a general framework for the analysis of human factors and ergonomics in employing diverse teleoperation interfaces. The proposed framework will go beyond the traditional subjective analyses of usability by complementing it with online measurements of human body configurations. As a result, multiple quantitative metrics, such as joints’ usage, range of motion comfort, center of mass divergence, and posture comfort, are introduced. To demonstrate the potential of the proposed framework, two different teleoperation interfaces are considered, and real-world experiments with 11 participants performing a simulated industrial remote pick-and-place task are conducted. The quantitative results of this analysis are provided, and compared with subjective questionnaires, illustrating the effectiveness of the proposed framework.

Work-related musculoskeletal disorders (WMSDs) remain one of the major occupational safety and health problems in the European Union nowadays. Thus, continuous tracking of workers’ exposure to the factors that may contribute to their develop- ment is paramount. This article introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to WMSDs, also giving importance to the subject-specific requirements of the workers. To evaluate the proposed framework, a thorough experimental analysis was conducted on 12 human subjects considering tasks that represent typical working activities in the manufacturing sector. For each task, the ergonomic indexes that better explain the underlying physical load were identified, following a statistical analysis, and supported by the outcome of a surface electromyography analysis. A comparison was also made with a well-recognized and standard tool to evaluate human ergonomics in the workplace, to highlight the benefits introduced by the proposed framework. Results demonstrate the high potential of the proposed framework in identifying the physical risk factors, and therefore, to adopt preventive measures. Another equally important contribution of this article is the creation of a comprehensive database on human kinodynamic measurements, which hosts multiple sensory data of healthy subjects performing typical industrial tasks.

Today’s robots are expected to fulfill different requirements originated from executing complex tasks in uncertain environments, often in collaboration with humans. To deal with this type of multi-objective control problem, hierarchical least-square optimization techniques are often employed, defining multiple tasks as objective functions, listed in hierarchical manner. The solution to the Inverse Kinematics problem requires to plan and constantly update the Cartesian trajectories. However, we propose an extension to the classical Hierarchical Quadratic Programming formulation, that allows to optimally generate these trajectories at control level. This is achieved by augmenting the optimization variable, to include the Cartesian reference and allow for the formulation of an adaptive compliance controller, which retains an impedancelike behaviour under external disturbances, while switching to an admittance-like behavior when collaborating with a human. The effectiveness of this approach is tested using a 7-DoF Franka Emika Panda manipulator in three different collaborative scenarios.

Prolonged remote tele-locomanipulation of multi degrees-of-freedom mobile manipulators requires a compromise between the system’s performance and the operator’s ergonomics. Neglecting this demand can significantly affect either the task completion or the level of comfort to achieve it. However, the simultaneous consideration of these key factors has received less attention in the literature. To respond to this demand, in this work, we introduce a new teleoperation setup, which integrates the features of an ergonomic and a highly maneuverable interface into a unified solution. The ergonomic part of the interface implements a 3D mouse-like functionality, enabling the execution of long navigation tasks for the floating base. The highly manoeuvrable interface instead, enables the operator to perform dynamic or more precise manipulation by moving his/her arm in space. The locomotion and manipulation modes of the follower robot are controlled separately, which can be easily and seamlessly switched by the operator by pressing a button at any moment. Furthermore, due to the follower manipulator’s redundancy, this robot is controlled by a hierarchical quadratic programming technique which enables the definition of a set of secondary tasks to be executed in the robot’s nullspace. Finally, to demonstrate the advantages and disadvantages of the proposed user interfaces, five participants are asked to perform two different experiments: (i) target selection task on a moving surface and (ii) remote path tracking on a fixed surface. The quantitative and qualitative analyses show the effectiveness of the proposed interface during the teleoperation tasks, especially when it comes to the precise and dynamic task execution.

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.

This paper proposes a novel integrated dynamic method based on Behavior Trees for planning and allocating tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal and logic constraints. In this way, instead of the well-studied offline centralized optimization problem, the role allocation problem is solved with multiple simplified online optimization sub-problem, without complex and cross-schedule task dependencies. These sub-problems are defined as Mixed-Integer Linear Programs, that, according to the worker-actions related costs and the workers' availability, allocate the yet-to-execute tasks among the available workers. To characterize the behavior of the developed method, we opted to perform different simulation experiments in which the results of the action-worker allocation and computational complexity are evaluated. The obtained results, due to the nature of the algorithm and to the possibility of simulating the agents' behavior, should describe well also how the algorithm performs in real experiments.

In this article, we present an online scalable tele-impedance framework, which enables the individual and collaborative control of multiple different robotic platforms. The framework provides an intuitive low-cost interface with visual feedback and a SpaceMouse, through which the operator can define the desired task-level trajectories and impedance profiles. With a simple mouse click, the user can switch between the robots and the collaborative operation mode. The control, subsequently, manages the distribution of the required parameters into the involved robots. Thanks to the introduced virtual hand concept, where each robot is defined as a finger, new robots can be easily added or removed via their kinodynamic parameters. The proposed framework was evaluated with three different experiments: a simulated auscultation on a mock-up patient, a cooperative task where a robot drives the patient on a wheelchair and a different robot performs the auscultation, and a collaborative task where two robots relocate a container. The results demonstrate the capabilities of the framework in terms of adaptability to different robotic platforms, the number of robots involved, and the task requirements. Additionally, quantitative and subjective analysis of 12 subjects showed how the developed interface, even in the presence of inaccurate visual feedback, allowed a smooth and accurate execution of the tasks.

The objective of this paper is to create a new collaborative robotic system that subsumes the advantages of mobile manipulators and supernumerary limbs. By exploiting the reconfiguration potential of a MObile Collaborative robot Assistant (MOCA), we create a collaborative robot that can function autonomously, in close proximity to humans, or be physically coupled to the human counterpart as a supernumerary body (MOCA-MAN). Through an admittance interface and a hand gesture recognition system, the controller can give higher priority to the mobile base (e.g., for long distance co-carrying tasks) or the arm movements (e.g., for manipulating tools), when performing conjoined actions. The resulting system has a high potential not only to reduce waste associated with the equipment waiting and setup times, but also to mitigate the human effort when performing heavy or prolonged manipulation tasks. The performance of the proposed system, i.e., MOCA-MAN, is evaluated by multiple subjects in two different use-case scenarios, which require large mobility or close-proximity manipulation.

In this paper, a novel human-robot collaborative framework for mixed case palletizing is presented. The framework addresses several challenges associated with the detection and localisation of boxes and pallets through visual perception algorithms, high-level optimisation of the collaborative effort through effective role-allocation principles, and maximisation of packing density. A graphical user interface (GUI) is additionally developed to ensure an intuitive allocation of roles and the optimal placement of the boxes on target pallets. The framework is evaluated in two conditions where humans operate with and without the support of a Mobile COllaborative robotic Assistant (MOCA). The results show that the optimised placement can improve up to the 20% with respect to a manual execution of the same task, and reveal the high potential of MOCA in increasing the performance of collaborative palletizing tasks.

The objective of this paper is to present a personalisable human ergonomics framework that integrates a method for real-time identification of a human model and an ergonomics monitoring function. The human model is based on a floating base structure and on a Statically Equivalent Serial Chain (SESC) model used for the estimation of the whole-body centre of Mass (CoM). A recursive linear regression algorithm (i.e., Kalman filter) is developed to achieve the online identification of the SESC parameters. A visual feedback provides a minimum set of suggested human poses to speed up the identification process, while enhancing the model accuracy based on a convergence value. The online ergonomics monitoring function computes and displays the overloading effects on body joints in heavy lifting tasks. The overloading joint torques are calculated based on the displacement of the Center of Pressure (CoP) between the measured one and the estimated one. Unlike our previous work, the entire process, from the model identification (personalisation) to ergonomics monitoring, is performed in real-time. We evaluated the efficacy of the proposed method through human experiments during model identification and load lifting tasks. Results demonstrate the high exploitation potential of the framework in industrial settings, due to its fast personalisation and ergonomics monitoring capacity.

Full teleoperation of mobile robots during the execution of complex tasks not only demands high cognitive and physical effort but also generates less optimal trajectories compared to autonomous controllers. However, the use of the latter in cluttered and dynamically varying environments is still an open and challenging topic. This is due to several factors such as sensory measurement failures and rapid changes in task requirements. Shared-control approaches have been introduced to overcome these issues. However, these either present a strong decoupling that makes them still sensitive to unexpected events, or highly complex interfaces only accessible to expert users. In this work, we focus on the development of a novel and intuitive shared-control framework for target detection and control of mobile robots. The proposed framework merges the information coming from a teleoperation device with a stochastic evaluation of the desired goal to generate autonomous trajectories while keeping a human-in-control approach. This allows the operator to react in case of goal changes, sensor failures, or unexpected disturbances. The proposed approach is validated through several experiments both in simulation and in a real environment where the users try to reach a chosen goal in the presence of obstacles and unexpected disturbances. Operators receive both visual feedback of the environment and voice feedback of the goal estimation status while teleoperating a mobile robot through a control-pad. Results of the proposed method are compared to pure teleoperation proving a better time-efficiency and easiness-of-use of the presented approach.

In this work, we propose a novel visuo-haptic guidance interface to enable mobile collaborative robots to follow human instructions in a way understandable by non-experts. The interface is composed of a haptic admittance module and a human visual tracking module. The haptic guidance enables an individual to guide the robot end-effector in the workspace to reach and grasp arbitrary items. The visual interface, on the other hand, uses a real-time human tracking system and enables autonomous and continuous navigation of the mobile robot towards the human, with the ability to avoid static and dynamic obstacles along its path. To ensure a safer human-robot interaction, the visual tracking goal is set outside of a certain area around the human body, entering which will switch robot behaviour to the haptic mode. The execution of the two modes is achieved by two different controllers, the mobile base admittance controller for the haptic guidance and the robot's whole-body impedance controller, that enables physically coupled and controllable locomotion and manipulation. The proposed interface is validated experimentally, where a human-guided robot performs the loading and transportation of a heavy object in a cluttered workspace, illustrating the potential of the proposed Follow-Me interface in removing the external loading from the human body in this type of repetitive industrial tasks.

The objective of this paper is to present a mathematical tool for real-time tracking of whole-body compressive forces induced by external physical solicitations. This tool extends and enriches our recently introduced ergonomics monitoring system to asses the level of risk associated with human physical activities in human-robot collaboration contexts. The methods developed so far only considered the effect of the external loads on joint torque variations. However, even for negligible values of the joint torque overloadings (e.g., in singular configurations), the effect of compressive forces, defined by the internal/pushing forces among body links, can be significant. Accordingly, we propose the joint compressive forces as an additional real-time index for the assessment of human ergonomics. First, a simulation study is performed to validate the method. Then, follows a laboratory study on five subjects to compare the trend of the joint compressive forces with muscle activities. Results demonstrate the significance of the proposed index in the development of a comprehensive human ergonomics monitoring framework. Based on such a framework, robotic strategies as well as feedback interfaces can be employed to guide and optimise the human movement toward more convenient body configurations thus avoiding pain and consequent injuries.

Autonomous goal detection and navigation control of mobile robots in remote environments can help to unload human operators from simple, monotonous tasks allowing them to focus on more cognitively stimulating actions. This can result in better task performances, while creating user-interfaces that are understandable by non-experts. However, full autonomy in unpredictable and dynamically changing environments is still far from becoming a reality. Thus, teleoperated systems integrating the supervisory role and instantaneous decision-making capacity of humans are still required for fast and reliable robotic operations. This work presents a novel shared-autonomy framework for goal detection and navigation control of mobile manipulators. The controller exploits human-gaze information to estimate the desired goal. This is used together with control-pad data to predict user intention, and to activate the autonomous control for executing a target task. Using the control-pad device, a user can react to unexpected disturbances and halt the autonomous mode at any time. By releasing the control-pad device (e.g., after avoiding an instantaneous obstacle) the controller smoothly switches back to the autonomous mode and navigates the robot towards the target. Experiments for reaching a target goal in the presence of unknown obstacles are carried out to evaluate the performance of the proposed shared-autonomy framework over seven subjects. The results prove the accuracy, time-efficiency, and ease-of-use of the presented shared-autonomy control framework.

The objective of this paper is to propose an adaptive impedance control framework to cope with uncertainties in vision and dynamics in robotic assembly tasks. The framework is composed of an adaptive controller, a vision system, and an interaction planner, which are all supervised by a finite state machine. In this framework, the target assembly object's pose is detected through the vision module, which is then used for the planning of the robot trajectories. The adaptive impedance control module copes with the uncertainties of the vision and the interaction planner modules in alignment of the assembly parts (a peg and a hole in this work). Unlike the classical impedance controllers, the online adaptation rule regulates the level of robot compliance in constrained directions, acting on and responding to the external forces. This enables the implementation of a flexible and adaptive Remote Center of Compliance (RCC) system, using active control. We first evaluate the performance of the proposed adaptive controller in comparison to classical impedance control. Next, the overall performance of the integrated system is evaluated in a peg-in-hole setup, with different clearances and orientation mismatches.

Nowadays, robots are expected to enter in various application scenarios and interact with unknown and dynamically changing environments. This highlights the need for creating autonomous robot behaviours to explore such environments, identify their characteristics and adapt, and build knowledge for future interactions. To respond to this need, in this paper we present a novel framework that integrates multiple components to achieve a context-aware and adaptive interaction between the robot and uncertain environments. The core of this framework is a novel self-tuning impedance controller that regulates robot quasi-static parameters, i.e., stiffness and damping, based on the robot sensory data and vision. The tuning of the parameters is achieved only in the direction(s) of interaction or movement, by distinguishing expected interactions from external disturbances. A vision module is developed to recognize the environmental characteristics and to associate them to the previously/newly identified interaction parameters, with the robot always being able to adapt to the new changes or unexpected situations. This enables a faster robot adaptability, starting from better initial interaction parameters. The framework is evaluated experimentally in an agricultural task, where the robot effectively interacts with various deformable environments.

Humans commonly exploit interaction with the environment constraints to assist the execution of the loco-manipulation tasks they perform. One particular example is the exploration of contacts during manipulation to relax the loading of those arm joints that are not directly involved in the generation of the manipulation motions and forces, e.g. establishing a contact with the elbow joint to reduce the effort of the upper arm while executing wrist level manipulation. In this paper, we shall explore the possibility of actively (a) utilizing the environment for a non-end-effector support contact towards reducing the joints efforts during manipulation tasks. This is achieved by our proposed control scheme with a three-level hierarchical compliance controller. The highest priority task is assigned to an impedance control that regulates the interaction at the contact control point on the arm in the normal direction of the support plane prior to contact, and is switched to an optimal contact force control for minimizing the joint effort after the contact is built. The second priority task is an impedance control at the same point in the tangential directions of the plane to stabilize the contact. In the end, an impedance behavior at the end-effector is designed to deal with the interaction forces required by the manipulation tasks. The efficacy of the proposed control scheme was corroborated by simulations and experiments, where significant joint effort reduction was observed.

Among the numerous risk factors associated to work-related musculoskeletal disorders (WMSD), repetitive and monotonous movements with light-weight tools are one of the most frequently cited. Such tasks may indeed result in the excessive accumulation of local muscle fatigue, causing severe injuries in human joints. Accordingly, this paper proposes a new whole-body fatigue model to evaluate the cumulative effect of the overloading torque induced on the joints over time by light payloads. The proposed model is then integrated into a human-robot collaboration (HRC) framework to set the timing of a body posture optimisation procedure guided by the robot assistance, by the time fatigue overcomes a threshold in any joint. Our overloading fatigue model is based on an estimation method we developed in a previous work, to monitor joint torque variations due to external forces in real-time. To account for individuals' different perception of fatigue, the fatigue ratio parameter in the model is computed experimentally for each subject. The proposed model is first studied on ten subjects by means of an electromyography analysis. Next, its performance is assessed in a painting task and finally evaluated within the HRC framework, which is proved to be able to reduce the risk of injuries caused by excessive fatigue accumulation.

In this paper, we propose a control framework for a multi-human and mobile-robot collaborative team, that takes into account the co-workers' ergonomic requirements as well as the demand for high flexibility in the manufacturing industries. The new MObile Collaborative robotic Assistant (MOCA), which is composed of a lightweight manipulator arm, an underactuated hand, and a mobile platform driven by four omni-directional wheels enabling mobility in the workspace, is able to accomplish multiple tasks in a wide area with a high level of adaptability. In addition, an ergonomics module to anticipate and mitigate the human risk factors by means of a multi-object optimisation is integrated into the framework to ensure human safety and improvement of working conditions. The main advantage of this approach is that MOCA can assist multiple human operators, reducing their physical risks, with fast-adaptive capacities due to agile mobility and advanced interaction and manipulation. We validated the proposed method with an experiment simulating a simple manufacturing line which involves two subjects and the MOCA. The results demonstrate that the proposed framework is able to address multi-workers' ergonomics with a high level of flexibility in the workplace.

This paper has developed a coordination control method for a dual-arm exoskeleton robot based on human impedance transfer skills, where the left (master) robot arm extracts the human limb impedance stiffness and position profiles, and then transfers the information to the right (slave) arm of the exoskeleton. A computationally efficient model of the arm endpoint stiffness behavior is developed and a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. A reference command consisting of the stiffness and position profiles of the operator is computed and realized by one robot in real-time. Considering the dynamics uncertainties of the robotic exoskeleton, an adaptive-robust impedance controller in task space is proposed to drive the slave arm tracking the desired trajectories with convergent errors. To verify the robustness of the developed approach, a study of combining adaptive control and human impedance transfer control under the presence of unknown interactive forces is conducted. The experimental results of this paper suggest that the proposed control method enables the subjects to execute a coordination control task on a dual-arm exoskeleton robot by transferring the stiffness from the human arm to the slave robot arm, which turns out to be effective.

Complex interactions with unstructured environments require the application of appropriate restoring forces in response to the imposed displacements. Impedance control techniques provide effective solutions to achieve this, however, their quasi-static performance is highly dependent on the choice of parameters, i.e. stiffness and damping. In most cases, such parameters are previously selected by robot programmers to achieve a desired response, which limits the adaptation capability of robots to varying task conditions. To improve the generality of interaction planning through task-dependent regulation of the parameters, this paper introduces a novel self-regulating impedance controller. The regulation of the parameters is achieved based on the robot's local sensory data, and on an interaction expectancy value. This value combines the interaction values from the robot state machine and visual feedback, to authorize the autonomous tuning of the impedance parameters in selective Cartesian axes. The effectiveness of the proposed method is validated experimentally in a debris removal task.

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.

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.

This paper aims at the development of a unified method for online identification and tracking of a kinematic musculoskeletal model of human arm to pave the way for related realtime applications, such as human-robot interaction, teleoperation and biomedical analysis. In order to decouple the identification of the joint angles of human arm kinematic model from a variety of motion capture (MoCap) setups, a generalized human arm triangle, which can be easily calculated by raw motion data, is introduced as an intermediate unified expression interface of human arm posture. An analytical solution to the Inverse Kinematics (IK) problem from the proposed human arm triangle to the joint angles of a commonly used OpenSim human right arm model is derived in detail. Once the human arm kinematic model is reconstructed, the involved muscles can be located correspondingly for related applications. Comparative simulation and experiment are conducted to validate the performance of the proposed IK and the whole tracking method. The results manifest that the calculation efficiency of the proposed IK can achieve an enormous speedup of 400-600 times with respect to the OpenSim built-in IK while maintaining comparable accuracy. Therefore, the proposed method can be an important tool to enable many online applications using human arm musculoskeletal model.

The understanding of human arm stiffness have brought several significant advances to robotics. For the most part, the end-point stiffness of human arm serves as an important role in guiding and shaping the Cartesian stiffness of robot arm in the execution of complicated interaction tasks because of the convenience of using the common space where both stiffnesses function. However, investigation of the joint stiffness of human arm, on the other hand, will provide a more comprehensive perspective on the human arm stiffness and enable other appealing robotic applications, for instance, whole-arm interaction with unstructured environment. As a fundamental research for these applications, the feasibility of an online joint stiffness transfer approach from human to anthropomorphic arms is discussed in this paper. This is realized by a proposed concept of physiological joint stiffness, which is shared by the human and anthropomorphic arms. The desired joint stiffness of robot arm is transformed from the estimated joint stiffness of human arm by requiring both arms to have the same apparent physiological joint stiffness. To make the calculated joint stiffness achievable in a robot controller, the stiffness matrix is subsequently optimized to be symmetric and positive definite. Proof-of-concept experiment is performed on a fully integrated robotic teleoperation setup to validate the efficacy of the proposed method.

In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator's motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject's movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject's muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking.

Tele-operation in the presence of environmental constraints is a well-studied problem, where the difficulties of the transparency-stability trade-off have been elucidated by several important studies. While at the state-of-art, passivity-based stabilizers appear to provide the best insight and command over this problem, recent work by our group has proposed an alternative approach, which consists in measuring and replicating the master's limb endpoint impedance on the slave robot in real-time. Tele-impedance control offers advantages in certain conditions, e.g. where master-slave communications are low quality. However, force feedback remains necessary when visual feedback is impaired or transparency and telepresence in the remote environment is of major concern. In this paper, we propose a novel framework to achieve the Tele-Impedance with Force Feedback (TIFF) so as to have a seamless control scheme that subsumes the performance advantages of both, while still guaranteeing stability and transparency. Experimental results illustrate the potential of the proposed technique in addressing the drawbacks of the two concepts.

In humanoids and other redundant robots interacting with the environment, one can often choose between different configurations and control parameters to achieve a given task. A classic tool to describe specifications of the desired force/displacement behavior in such problems is the stiffness ellipsoid, whose geometry is affected by the choice of parameters in both joint control and redundancy resolution-namely, gains and angles. As is well known, impedance control techniques can regulate gains to realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector, so that robot geometry selection could appear secondary. However, humans do not use this possibility: To control the stiffness of our arms, we predominantly use arm configurations. Why is that, and does it makes sense to do the same in robots? To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of compliant redundant arms to illustrate why the arm geometry plays a dominant role in interaction capabilities of robots. We introduce the notion of allowable Cartesian force/displacement (“stiffness feasibility”) regions (SFR) for compliant robots with given torque boundaries. We show that different robot configurations modify such regions and explore the role of robot geometry in achieving an appropriate SFR for the task at hand. The novel concepts and definitions are first illustrated in simulations. Experimental results are then provided to verify the effectiveness of the proposed Cartesian force and stiffness control.

As a major inspiration of biologically inspired systems, multi-legged robots have been developed due to their superior stability feature resulting from their large support polygon. The leg design of a majority of such robots is motivated by the skeleton of vertebrates such as dogs, or that of invertebrates such as spiders. Despite a wide variety of multi-pedal robots on the basis of the two aforesaid leg designs, a thorough comparison of the two underlying design principles remains to be done. This work addresses this problem and presents a comparative study for the two mammal-like and spider-like designs by looking at the joint torque profile, the responsive motion of the legs, and the thrust force applied by the robot. To this end, a set of performance indexes are defined based on the gravity compensation torque, the dynamic manipulability polytope and the force polytope, and evaluated in various leg configurations of the two designs.

In this work, to guarantee the Pisa/IIT SoftHand's grasp robustness against slippage, three reflex control modes, namely Current, Pose and Impedance, are implemented and experimentally evaluated. Towards this objective, ThimbleSense fingertip sensors are designed and integrated into the thumb and middle fingers of the SoftHand for real-time detection and control of the slippage. Current reflex regulates the restoring grasp forces of the hand by modulating the motor's current profile according to an update law. Pose and Impedance reflex modes instead replicate this behaviour by implementing an impedance control scheme. The difference between the two latter is that the stiffness gain in Impedance reflex mode is being varied in addition to the hand pose, as a function of the slippage on the fingertips. Experimental setup also includes a seven degrees-of-freedom robotic arm to realize consistent trajectories (e.g. lifting) among three control modes for the sake of comparison. Different test objects are considered to evaluate the efficacy of the proposed reflex modes in our experimental setup. Results suggest that task-appropriate restoring forces can be achieved using Impedance reflex due to its capability in demonstrating instantaneous and rather smooth reflexive behaviour during slippage. Preliminary experiments on five healthy human subjects provide evidence on the similarity of the control concepts exploited by the humans and the one realized by the Impedance reflex, highlighting its potential in prosthetic applications.

In this work a novel synergy-based bilateral tele-manipulation strategy is introduced. The proposed algorithm has been primarily developed to remotely control the Pisa/IIT SoftHand (SH) using a 3-finger hand exoskeleton as master device. With a single actuator and a sensory system limited to a position encoder and a current sensor, the SH minimalist design promotes robustness but challenges traditional teleoperation strategies. To tackle this challenge, the concept of Cartesian-based hand synergies is introduced as a projection tool which maps the fingertip Cartesian space to the directions oriented along the grasp principal components. The unconstrained motion of the operator's hand is projected on this space to extract the SH's motor position reference. Conversely, the interaction force estimated at the robotic hand as a 1-dimensional force along the first synergy is projected to the 9D fingertip Cartesian space through an inverse projection. The resultant finger-individualized forces form a synergy based weighted representation of the grasping effort applied by the SH and are displayed to the operators fingertips using the force feedback hand exoskeleton. The system's ability to reflect the environment's impedance has been experimentally validated during a ball squeezing experiment. To assess the overall effectiveness of the proposed system as a manipulation interface, the SoftHand was mounted on the humanoid robot COMAN and the setup was subsequently enriched with a vision-based tracking system monitoring the operators wrist trajectory. Experimental results indicate that the proposed body-machine bilateral interface allows for the intuitive performance of stable grasps and transport of a large range of diversely shaped objects.

This paper presents a novel approach for human-robot cooperation in tasks with dynamic uncertainties. The essential element of the proposed method is a multi-modal interface that provides the robot with the feedback about the human motor behaviour in real-time. The human muscle activity measurements and the arm force manipulability properties encode the information about the motion and impedance, and the intended configuration of the task frame, respectively. Through this human-in-the-loop framework, the developed hybrid controller of the robot can adapt its actions to provide the desired motion and impedance regulation in different phases of the cooperative task. We experimentally evaluate the proposed approach in a two-person sawing task that requires an appropriate complementary behaviour from the two agents.

The tutor-tutee hand-in-hand teaching may be the most effective approach for a tutee to acquire new motor skills. Repetitive nature of such procedures in a group setting usually results in a high labour cost and time inefficiency. Potential solution can be utilizing robotic platforms playing the role of tutors for demonstrating and transferring the required skills. This requires an appropriate guidance scheme to integrate the tutor's motor functionalities into the robot's control architecture. For instance, for hand-in-hand supervision of the writing task, the tutor's corrections can be applied when necessary, while a very compliant motion can be achieved if no errors are detected. Inspired by this behavior, we develop a teaching interface using a dual-arm robotic platform. In our setup, one arm is connected to the tutees arm providing guidance through a variable stiffness control approach, and the other to the tutor to capture the motion and to feedback the tutees performance in a haptic manner. The reference stiffness for the tutors arm stiffness is estimated in real-time and replicated by the tutees robotic arm. Comparative experiments have been carried out on a dual-arm Baxter robot. The results imply that the human tutor is able to intuitively transfer writing skills to the tutee and also show superior learning performance over over some conventional teaching by demonstration techniques.

The stiffness ellipsoid, i.e. the locus of task-space forces obtained corresponding to a deformation of unit norm in different directions, has been extensively used as a powerful representation of robot interaction capabilities. The size and shape of the stiffness ellipsoid at a given end-effector posture are influenced by both joint control parameters and - for redundant manipulators - by the chosen redundancy resolution configuration. As is well known, impedance control techniques ideally provide control parameters which realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector in an arbitrary non-singular configuration, so that arm geometry selection could appear secondary. This definitely contrasts with observations on how humans control their arm stiffness, who in fact appear to predominantly use arm configurations to shape the stiffness ellipsoid. To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of redundant arms, which explains why arm geometry also plays a fundamental role in interaction capabilities of a torque controlled robot. We show that stiffness control of realistic robot models with bounds on joint torques can't indeed achieve arbitrary stiffness ellipsoids at any given arm configuration. We first introduce the notion of maximum allowable Cartesian force/displacement (“stiffness feasibility”) regions for a compliant robot. We show that different robot configurations modify such regions, and explore the role of different configurations in defining the performance limits of Cartesian stiffness controllers. On these bases, we design a stiffness control method that suitably exploits both joint control parameters and redundancy resolution to achieve desired task-space interaction behavior.

It is well known that kinematics can significantly affect the manipulation capabilities of robotic arms, traditionally illustrated by performance indices such as workspace volume, kinematic and force manipulability, and isotropy within the arm workspace. In the case of dual-arm systems and bimanual manipulation tasks, the kinematics effects to the above indices becomes even more apparent. However, in spite of the large number of dual-arm systems developed in the past, there is a little literature on the kinematic design analysis for the development of such systems. Particularly, the effects of configuration/ orientation of the shoulders' placement with respect to the torso structure have not sufficiently studied or considered, while many dual-arm systems with upward and/or forward tilt angle in shoulder base frame have been introduced. This paper addresses this problem and quantifies the effect of shoulders base frame orientation in a dual-arm manipulation system by looking at its effect on several important manipulation indices, such as the overall and common workspace, redundancy, global isotropy, dual-arm manipulability, and inertia ellipsoid index within the common workspace of the two arms. Consequently, a range of upward and forward tilt angles for the shoulder frames is identified for the design of a dual-arm torso system to render the most desired manipulation performance.

Effective and stable execution of a remote manipulation task in an uncertain environment requires that the task force and position trajectories of the slave robot be appropriately commanded. To achieve this goal, in teleimpedance control, a reference command which consists of the stiffness and position profiles of the master is computed and realized by the compliant slave robot in real-time. This highlights the need for a suitable and computationally efficient tracking of the human limb stiffness profile in real-time. In this direction, based on the observations in human neuromotor control which give evidence on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the role of muscular co-activations which contribute to a coordinated regulation of the task stiffness in all directions, we propose a novel and computationally efficient model of the arm endpoint stiffness behaviour. Real-time tracking of the human arm kinematics is achieved using an arm triangle monitored by three markers placed at the shoulder, elbow and wrist level. In addition, a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and co-contraction levels of the chosen muscles. Results of this study suggest that the proposed model enables the master to naturally execute a remote task by modulating the direction of the major axes of the endpoint stiffness and its volume using arm configuration and the co-activation of the involved muscles, respectively.

Incorporation of human motor control principles in the motion control architectures for humanoid robots or assistive and prosthesis devices will permit these systems not only to look anthropomorphic and natural at the body ware level but also to generate natural motion profiles resembling those executed by humans during manipulation and locomotion. In this work, relying on the observations on human bimanual coordination, a novel realtime motion control strategy is proposed to regulate the desired Cartesian stiffness profile during the execution of bimanual tasks. The novelty of the proposed control scheme relies on the use of common mode stiffness (CMS) and configuration dependent stiffness (CDS) to regulate the size and directionality of the task space stiffness ellipsoid. Thanks to the CDS control, the proposed scheme is not only proved to be effective in regulating the desired stiffness ellipsoid but also permits to resolve the manipulator redundancy in a natural manner. The effectiveness of the controller is evaluated in an experimental setup in which two cooperating robotic arms are executing an assembly task. Experimental results demonstrate that the proposed dual-arm CDS-CMS controller is effective in tracking the desired stiffness ellipsoids as well as in producing human-like natural motions for the two robotic arms.

This work proposes a representation that comprises both shape and friction, as well as the exploration strategy to gather them from an object. The representation is developed under a common probabilistic framework, particularly it uses a Gaussian Process to approximate the distribution of the friction coefficient over the surface, also represented as a Gaussian Process. The surface model is exploited to compute straight lines (geodesic flows) that guide the exploration. The exploration follows these flows by employing an impedance controller in pursuance of safety, shape accommodation and contact enforcement, while measuring the necessary data to estimate the friction coefficient. The exploratory probes consist of an RGBD camera and an Intrinsic Tactile sensor (ITs) mounted on a robotic arm. Experimental results give evidence for the effectiveness of the algorithm in the friction coefficient gathering and enrichment of the object representation.

Humans incorporate and switch between learnt neuromotor strategies while performing complex tasks. Towards this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this paper, we investigate a combined free motion and interaction controller in a certain class of robotic manipulation. In this bimodal controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to minimum-effort controller while performing point to point free space movements. Once the robot comes in contact with the environment, the Tele-Impedance, common mode and configuration dependent stiffness (CMS-CDS) controller will replicate the human's estimated endpoint stiffness and measured equilibrium position profiles in the slave robotic arm, in real-time. Results of the proposed controller in contact with the environment are compared with the ones derived from Tele-Impedance implemented using torque based classical Cartesian stiffness control. The minimum-effort and interaction performance achieved highlights the possibility of adopting human-like and sophisticated strategies in humanoid robots or the ones with adequate degrees of redundancy, in order to accomplish tasks in a certain class of robotic manipulation.

In this paper, a knee exoskeleton device and its Tele-Impedance based assistive control scheme is presented. The exoskeleton device is an inherently compliant actuated system that was implemented based on the series elastic actuation (SEA) to provide improved and intrinsically soft interaction behaviour. Details of the exoskeleton design are presented. A detailed musculoskeletal model was developed and experimentally identified in order to map electromyographic signals to the antagonistic muscle torques, acting on the human knee joint. The estimated muscle torques are used in order to determine the user's intent and joint stiffness trend. These reference signals are exploited by a novel Tele-Impedance controller which is applied to a knee exoskeleton device to provide assistance and stiffness augmentation to the user's knee joint. Experimental trials of a standing-up motion task were carried out for evaluation of the proposed control strategy. The results indicate that the proposed knee exoskeleton device and control scheme can effectively generate assistive actions that are intrinsically and naturally controlled by the user muscle activity.

In this paper, a novel synergy driven teleimpedance controller for the Pisa-IIT SoftHand is presented. Towards the development of an efficient, robust, and low-cost hand prothesis, the Pisa-IIT SoftHand is built on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of objects of various shapes using only a single motor. In this work, the hand grasping motion is regulated with an impedance controller which incorporates the user's postural and stiffness synergy profiles in realtime. In addition, a disturbance observer is realized which estimates the grasping contact force. The estimated force is then fedback to the user via a vibration motor. Grasp robustness and transparency improvements were evaluated on two healthy subjects while grasping different objects. Implementation of the proposed teleimpedance controller led to the execution of stable grasps by controlling the grasping forces, via modulation of hand compliance. In addition, utilization of the vibrotactile feedback resulted in reduced physical load on the user. While these results need to be validated with amputees, they provide evidence that a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses.

This work presents the novel concept of Tele-Impedance as a method for controlling/teleoperating a robotic arm while performing tasks which require significant dynamics variation. As an alternative method to bilateral force-reflecting teleoperation control approach, which uses a position/velocity command combined with force feedback from the robot side, Tele-Impedance enriches the command sent to the slave robot by combining the position reference with a stiffness (or full impedance) reference estimated from the arm of the human operator. We propose a new method to estimate the stiffness of the human arm based on the agonist-antagonist muscular co activations. The concept of the Tele-Impedance is demonstrated using the KUKA light weight robotic arm as the slave manipulator in a ball reception experiment. The performance of Tele-Impedance control method is assessed by comparing the results obtained while receiving the ball, with the slave arm under i) constant low stiffness, ii) constant high stiffness or iii) under Tele-Impedance control. Performance indexes are defined and used for the comparative study of the ball reception performances under the different endpoint elastic profiles. The experimental results demonstrate the effectiveness of the task-related Tele-Impedance control method and highlight its potential use to execute tasks which require significant dynamics variation.