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Biped robots usually adopt feet with a rigid structure that simplifies walking on flat grounds and yet hinders ground adaptation in unstructured environments, thus jeopardizing stability. We recently explored in the SoftFoot the idea of adapting a robotic foot to ground irregularities along the sagittal plane. Building on the previous results, we propose in this paper a novel robotic foot able to adapt both in the sagittal and frontal planes, similarly to the human foot. It features five parallel modules with intrinsic longitudinal adaptability that can be combined in many possible designs through optional rigid or elastic connections. By following a methodological design approach, we narrow down the design space to five candidate foot designs and implement them on a modular system. Prototypes are tested experimentally via controlled application of force, through a robotic arm, onto a sensorized plate endowed with different obstacles. Their performance is compared, using also a rigid foot and the previous SoftFoot as a baseline. Analysis of footprint stability shows that the introduction of the transverse arch, by elastically connecting the five parallel modules, is advantageous for obstacle negotiation, especially when obstacles are located under the forefoot. In addition to biped robots’ locomotion, this finding might also benefit lower-limb prostheses design.

This paper presents an aerial platform capable of performing physically interactive tasks in unstructured environments with human-like dexterity under human supervision. This aerial platform consists of a humanoid torso attached to a hexacopter. A two-degree-of-freedom head and two five-degree-of-freedom arms equipped with SoftHands provide the requisite dexterity to allow human operators to carry out various tasks. A robust tendon-driven structure is purposefully designed for the arms, considerably reducing the impact of arm inertia on the aerial base in motion. In addition, tendons provide flexibility to the joints, which enhances the robustness of the arm preventing damage in interaction with the environment. To increase the payload of the aerial system and the battery life, we use the concept of Suspended Aerial Manipulation, i.e., the flying humanoid can be connected with a tether to a structure, e.g., a building, a fixed bracket, a supporting crane, or a larger airborne carrier. Importantly, to maximize portability and applicability, we adopt a modular approach exploiting commercial components for the aerial base hardware and autopilot. We develop an outer stabilizing control loop to maintain the attitude, compensating for the tether force and for the humanoid head and arm motions. The humanoid can be controlled by a remote operator, thus effectively realizing a Suspended Aerial Manipulation Avatar. The proposed system is validated through experiments in indoor scenarios reproducing post-disaster tasks.

Summary: For dexterous control of the hand, humans integrate sensory information and prior knowledge regarding their bodies and the world. We studied the role of touch in hand motor control by challenging a fundamental prior assumption—that self-motion of inanimate objects is unlikely upon contact. In a reaching task, participants slid their fingertips across a robotic interface, with their hand hidden from sight. Unbeknownst to the participants, the robotic interface remained static, followed hand movement, or moved in opposition to it. We considered two hypotheses. Either participants were able to account for surface motion or, if the stationarity assumption held, they would integrate the biased tactile cues and proprioception. Motor errors consistent with the latter hypothesis were observed. The role of visual feedback, tactile sensitivity, and friction was also investigated. Our study carries profound implications for human-machine collaboration in a world where objects may no longer conform to the stationarity assumption.

The objective of this paper is to propose a framework for a robot to learn multiple Sensory-Motor Contingencies from human demonstrations and reproduce them. Sensory-Motor Contingencies are a concept that describes intelligent behavior of animals and humans in relation to their environment. They have been used to design control and planning algorithms for robots capable of interacting and adapting autonomously. However, enabling a robot to autonomously develop Sensory-Motor Contingencies is challenging due to the complexity of action and perception signals. This framework leverages tools from Learning from Demonstrations to have the robot memorize various sensory phases and corresponding motor actions through an attention mechanism. This generates a metric in the perception space, used by the robot to determine which sensory-motor memory is contingent to the current context. The robot generalizes the memorized actions to adapt them to the present perception. This process creates a discrete lattice of continuous Sensory-Motor Contingencies that can control a robot in loco-manipulation tasks. Experiments on a 7-dof collaborative robotic arm with a gripper, and on a mobile manipulator demonstrate the functionality and versatility of the framework.

Abstract Background Among commercially-available upper-limb prostheses, the two most often used solutions are simple hook-style grippers and poly-articulated hands, which present a higher number of articulations and show a closer resemblance to biological limbs. In their majority, the former type of prostheses is body-powered, while the second type is controlled by myoelectric signals. Body-powered grippers are easy to control and allow a simple form of force feedback, frequently appreciated by users. However, they present limited versatility. Poly-articulated hands afford a wide range of grasp and manipulation types, but require enough residual muscle activation for dexterous control. Several factors, e.g. level of limb loss, personal preferences, cost, current occupation, and hobbies can influence the preference for one option over the other, and is always a result of the trade-off between system performance and users’ needs. Methods The SoftHand Pro (SHP) is an artificial hand platform that has 19 independent joints (degrees-of-freedom), but is controlled by a single input. The design of this prosthesis is inspired by the concept of postural synergies in motor control and implemented with soft-robotic technologies. Their combination provides increased robustness, safe interaction and the execution of diverse grasps. The potential of the SHP is fully unleashed when users learn how to exploit its features and create an intimate relationship between the technical aspects of the prosthesis design and its control by the user. Results The great versatility of the SoftHand Pro (a reasearch protpotype) permitted its adaptation to the user requirements. This was experienced by the SoftHand Pro Team during the preparation for different CYBATHLON events (from 2016 to 2020). The mixed power and dexterous hand operations required by each task of the race is inspired by everyday tasks. Our prosthesis was driven by different pilots, with different habits and backgrounds. Consequently, the hand control modality was customized according to the user’s preferences. Furthermore, the CYBATHLON tasks had some variations in this period, promoting the continuous development of our technology with a user-centered approach. In this paper, we describe the participation and preparation of the SoftHand Pro Team from 2016 to 2020 with three pilots and two different activation modalities, hybrid body-controlled and myoelectric control. Conclusions We introduced our pilots, the implementation of the two control modalities, and describe the successful participation in all CYBATHLON events. This work proves the versatility of the system towards the user’s preferences and the changes in the race requirements. Finally, we discussed how the CYBATHLON experience and the training in the real-world scenario have driven the evolution of our system and gave us remarkable insights for future perspectives.

Abstract In patients with subacute stroke, task specific training (TST) has been shown to accelerate functional recovery of the upper limb. However, many patients do not have sufficient active extension of the fingers to perform this treatment. In these patients, here we propose a new rehabilitation technique in which TST is performed through a soft robotic hand (SoftHand-X). In short, the extension of the robotic fingers is controlled by the patient through his residual, albeit minimal, active extension of the fingers or wrist, while the patient was required to relax the muscles to achieve full flexion of the robotic fingers. TST with SoftHand-X was attempted in 27 subacute stroke patients unable to perform TST due to insufficient active extension of the fingers. Four patients (14.8%) were able to perform the proposed treatment (10 daily sessions of 60 min each). They reported an excellent level of participation. After the treatment, both clinical score of spasticity and its electromyographic correlate (stretch reflex) decreased. In subacute stroke patients, TST using SoftHand-X is a well-accepted treatment, resulting in a decrease of spasticity. At present, it can be applied only in a small proportion of the patients who cannot perform conventional TST, though extensions are possible.

Abstract Characterizing post-stroke impairments in the sensorimotor control of arm and hand is essential to better understand altered mechanisms of movement generation. Herein, we used a decomposition algorithm to characterize impairments in end-effector velocity and hand grip force data collected from an instrumented functional task in 83 healthy control and 27 chronic post-stroke individuals with mild-to-moderate impairments. According to kinematic and kinetic raw data, post-stroke individuals showed reduced functional performance during all task phases. After applying the decomposition algorithm, we observed that the behavioural data from healthy controls relies on a low-dimensional representation and demonstrated that this representation is mostly preserved post-stroke. Further, it emerged that reduced functional performance post-stroke correlates to an abnormal variance distribution of the behavioural representation, except when reducing hand grip forces. This suggests that the behavioural repertoire in these post-stroke individuals is mostly preserved, thereby pointing towards therapeutic strategies that optimize movement quality and the reduction of grip forces to improve performance of daily life activities post-stroke.

Although recent technological developments in the field of bionic upper limb prostheses, their rejection rate remains excessively high. The reasons are diverse (e.g. lack of functionality, control complexity, and comfortability) and most of these are reported only through self-rated questionnaires. Indeed, there is no quantitative evaluation of the extent to which a novel prosthetic solution can effectively address users’ needs compared to other technologies. This manuscript discusses the challenges and limitations of current upper limb prosthetic devices and evaluates their functionality through a standard functional assessment, the Assessment of Capacity for Myoelectric Control (ACMC). To include a good representation of technologies, the authors collect information from participants in the Cybathlon Powered Arm Prostheses Race 2016 and 2020. The article analyzes 7 hour and 41 min of video footage to evaluate the performance of different prosthetic devices in various tasks inspired by activities of daily living (ADL). The results show that commercially-available rigid hands perform well in dexterous grasping, while body-powered solutions are more reliable and convenient for competitive environments. The article also highlights the importance of wrist design and control modality for successful execution of ADL. Moreover, we discuss the limitations of the evaluation methodology and suggest improvements for future assessments. With regard to future development, this work highlights the need for research in intuitive control of multiple degrees of freedom, adaptive solutions, and the integration of sensory feedback.

Grasping an object is one of the most common and complex actions performed by humans. The human brain can adapt and update the grasp dynamics through information received from sensory feedback. Prosthetic hands can assist with the mechanical performance of grasping, however currently commercially available prostheses do not address the disruption of the sensory feedback loop. Providing feedback about a prosthetic hand's grasp force magnitude is a top priority for those with limb loss. This study tested a wearable haptic system, i.e., the Clenching Upper-Limb Force Feedback device (CUFF), which was integrated with a novel robotic hand (The SoftHand Pro). The SoftHand Pro was controlled with myoelectrics of the forearm muscles. Five participants with limb loss and nineteen able-bodied participants completed a constrained grasping task (with and without feedback) which required modulation of the grasp to reach a target force. This task was performed while depriving participants of incidental sensory sources (vision and hearing were significantly limited with glasses and headphones). The data were analyzed with Functional Principal Component Analysis (fPCA). CUFF feedback improved grasp precision for participants with limb loss who typically use body-powered prostheses as well as a sub-set of able-bodied participants. Further testing, that is more functional and allows participants to use all sensory sources, is needed to determine if CUFF feedback can accelerate mastery of myoelectric control or would benefit specific patient sub-groups.

Robots that work in unstructured scenarios are often subjected to collisions with the environment or external agents. Accordingly, recently, researchers focused on designing robust and resilient systems. This work presents a framework that quantitatively assesses the balancing resilience of self-stabilizing robots subjected to external perturbations. Our proposed framework consists of a set of novel Performance Indicators (PIs), experimental protocols for the reliable and repeatable measurement of the PIs, and a novel testbed to execute the protocols. The design of the testbed, the control structure, the post-processing software, and all the documentation related to the performance indicators and protocols are provided as open-source material so that other institutions can replicate the system. As an example of the application of our method, we report a set of experimental tests on a two-wheeled humanoid robot, with an experimental campaign of more than 1100 tests. The investigation demonstrates high repeatability and efficacy in executing reliable and precise perturbations.

Abstract Notwithstanding the advancement of modern bionic hands and the large variety of prosthetic hands in the market, commercial devices still present limited acceptance and percentage of daily use. While commercial prostheses present rigid mechanical structures, emerging trends in the design of robotic hands are moving towards soft technologies. Although this approach is inspired by nature and could be promising for prosthetic applications, there is scant literature concerning its benefits for end-users and in real-life scenarios. In this work, we evaluate and assess the role and the benefits of soft robotic technologies in the field of prosthetics. We propose a thorough comparison between rigid and soft characteristics of two poly-articulated hands in 5 non-expert myo-electric prosthesis users in pre- and post-therapeutic training conditions. The protocol includes two standard functional assessments, three surveys for user-perception, and three customized tests to evaluate the sense of embodiment. Results highlight that rigid hands provide a more precise grasp, while soft properties show higher functionalities thanks to their adaptability to different requirements, intuitive use and more natural execution of activities of daily living. This comprehensive evaluation suggests that softness could also promote a quick integration of the system in non-expert users.

In this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the previous trials and some properly tuned learning gains. Sufficient conditions on these gains guarantee the monotonic convergence of the iterative process. However, the choice of the gains is heuristically hand-tuned given an approximated system model and no information on the disturbances. Thus, in the cases of inaccurate knowledge of the model or iteration-varying measurement errors, external disturbances, and delays, the convergence condition is unlikely to be verified at every iteration. To overcome this issue, we propose a robust convergence condition, which ensures the applicability of the pure feedforward control even if other classical conditions are not fulfilled for some trials due to the presence of disturbances. Furthermore, we quantify the upper bound of the nonrepetitive disturbance that the iterative algorithm is able to handle. Finally, we validate the convergence condition simulating the dynamics of a two degrees of freedom underactuated arm with elastic joints, where one is active, and the other is passive, and a Franka Emika Panda manipulator.

Background: Innovation in prosthetic devices for adults with upper limb loss is necessary to meet the demand for effective devices to optimize participation in daily activity. We evaluate the SoftHand Pro (SHP) as a terminal device to determine the application of this biologically-inspired prosthetic hand for use for a person with transradial limb deficiency. Method: This case study describes and measures the first use of the SHP by an individual with transradial limb deficiency in their home environment. This paper reports the features and functionality of the SHP prototype and provides recommendations for changes to further optimize function. Results: The participant found the simple mechanics, durability, and ease of use of the SHP to be beneficial. She praised the SHP’s positive impact on quality of life and suggested areas for optimization. Objective assessments of dexterity and function showed improvements. Conclusion: Using a biologically-inspired myoelectric hand provides an opportunity for intuitively controlled grasp of common large and small objects. The simplicity of use and the lightweight, durable design of the SHP has the potential to provide a positive impact on quality of life, and this case study has provided valuable feedback to further improve the hand and enable larger at-home trials.

Abstract Background State-of-the-art bionic hands incorporate hi-tech devices which try to overcome limitations of conventional single grip systems. Unfortunately, their complexity often limits mechanical robustness and intuitive prosthesis control. Recently, the translation of neuroscientific theories (i.e. postural synergies) in software and hardware architecture of artificial devices is opening new approaches for the design and control of upper-limb prostheses. Methods Following these emerging principles, previous research on the SoftHand Pro, which embeds one physical synergy, showed promising results in terms of intuitiveness, robustness, and grasping performance. To explore these principles also in hands with augmented capabilities, this paper describes the SoftHand 2 Pro, a second generation of the device with 19 degrees-of-freedom and a second synergistic layer. After a description of the proposed device, the work explores a continuous switching control method based on a myoelectric pattern recognition classifier. Results The combined system was validated using standardized assessments with able-bodied and, for the first time, amputee subjects. Results show an average improvement of more than 30% of fine grasp capabilities and about 10% of hand function compared with the first generation SoftHand Pro. Conclusions Encouraging results suggest how this approach could be a viable way towards the design of more natural, reliable, and intuitive dexterous hands.

Abstract Background Human-likeliness of robot movements is a key component to enable a safe and effective human-robot interaction, since it contributes to increase acceptance and motion predictability of robots that have to closely interact with people, e.g. for assistance and rehabilitation purposes. Several parameters have been used to quantify how much a robot behaves like a human, which encompass aspects related to both the robot appearance and motion. The latter point is fundamental to allow the operator to interpret robotic actions, and plan a meaningful reactions. While different approaches have been presented in literature, which aim at devising bio-aware control guidelines, a direct implementation of human actions for robot planning is not straightforward, still representing an open issue in robotics. Methods We propose to embed a synergistic representation of human movements for robot motion generation. To do this, we recorded human upper-limb motions during daily living activities. We used functional Principal Component Analysis (fPCA) to extract principal motion patterns. We then formulated the planning problem by optimizing the weights of a reduced set of these components. For free-motions, our planning method results into a closed form solution which uses only one principal component. In case of obstacles, a numerical routine is proposed, incrementally enrolling principal components until the problem is solved with a suitable precision. Results Results of fPCA show that more than 80% of the observed variance can be explained by only three functional components. The application of our method to different meaningful movements, with and without obstacles, show that our approach is able to generate complex motions with a very reduced number of functional components. We show that the first synergy alone accounts for the 96% of cost reduction and that three components are able to achieve a satisfactory motion reconstruction in all the considered cases. Conclusions In this work we moved from the analysis of human movements via fPCA characterization to the design of a novel human-like motion generation algorithm able to generate, efficiently and with a reduced set of basis elements, several complex movements in free space, both in free motion and in case of obstacle avoidance tasks.

Poly-articulated hands, actuated by multiple motors and controlled by surface myoelectric technologies, represent the most advanced aids among commercial prostheses. However, simple hook-like body-powered solutions are still preferred for their robustness and control reliability, especially for challenging environments (such as those encountered in manual work or developing countries). This study presents the mechatronic implementation and the usability assessment of the SoftHand Pro-Hybrid, a family of poly-articulated, electrically-actuated, and body-controlled artificial hands, which combines the main advantages of both body-powered and myoelectric systems in a single device. An assessment of the proposed system is performed with individuals with and without limb loss, using as a benchmark the SoftHand Pro, which shares the same soft mechanical architecture, but is controlled using surface electromyographic sensors. Results indicate comparable task performance between the two control methods and suggest the potential of the SoftHand Pro-Hybrid configurations as a viable alternative to myoelectric control, especially in work and demanding environments.

To enable the design of planning and control strategies in simulated environments before their direct application to the real robot, exploiting the Sim2Real practice, powerful and realistic dynamic simulation tools have been proposed, e.g., the ROS-Gazebo framework. However, the majority of such simulators do not account for some of the properties of recently developed advanced systems, e.g., dynamic elastic behaviors shown by all those robots that purposely incorporate compliant elements into their actuators, the so-called Articulated Soft Robots ASRs. This paper presents an open-source ROS-Gazebo toolbox for simulating ASRs equipped with the aforementioned types of compliant actuators. To achieve this result, the toolbox consists of two ROS-Gazebo modules: a plugin that implements the custom compliant characteristics of a given actuator and simulates the internal motor dynamics, and a Robotic Operation System (ROS) manager node used to organize and simplify the overall toolbox usage. The toolbox can implement different compliant joint structures to perform realistic and representative simulations of ASRs, also when they interact with the environment. The simulated ASRs can be also used to retrieve information about the physical behavior of the real system from its simulation, and to develop control policies that can be transferred back to the real world, leveraging the Sim2Real practice. To assess the versatility of the proposed plugin, we report simulations of different compliant actuators. Then, to show the reliability of the simulated results, we present experiments executed on two ASRs and compare the performance of the real hardware with the simulations. Finally, to validate the toolbox effectiveness for Sim2Real control design, we learn a control policy in simulation, then feed it to the real system in feed-forward comparing the results.

The most common causes of the risk of work-related musculoskeletal disorders (WMSD) have been identified as joint overloading, bad postures, and vibrations. In the last two decades, various solutions ranging from human-robot collaborative systems to robotic exoskeletons have been proposed to mitigate them. More recently, a new approach has been proposed with a high potential in this direction: the supernumerary robotic limbs SRLs are additional robotic body parts (e.g., fingers, legs, and arms) that can be worn by the workers, augmenting their natural ability and reducing the risks of injuries. These systems are generally proposed in the literature for their potentiality of augmenting the user’s ability, but here we would like to explore this kind of technology as a new generation of (personal) protective equipment. A supernumerary robotic upper limb, for example, allows for indirectly interacting with hazardous objects like chemical products or vibrating tools. In particular, in this work, we present a supernumerary robotic limbs system to reduce the vibration transmitted along the arms and minimize the load on the upper limb joints. For this purpose, an off-the-shelf wearable gravity compensation system is integrated with a soft robotic hand and a custom damping wrist, designed starting from theoretical considerations on a mass-spring-damper model. The real efficacy of the system was experimentally tested within a simulated industrial work environment, where seven subjects performed a drilling task on two different materials. Experimental analysis was conducted according to the ISO-5349. Results showed a reduction from 40 to 60% of vibration transmission with respect to the traditional hand drilling using the presented SRL system without compromising the time performance.

Upper-limb impairments are all-pervasive in Activities of Daily Living (ADLs). As a consequence, people affected by a loss of arm function must endure severe limitations. To compensate for the lack of a functional arm and hand, we developed a wearable system that combines different assistive technologies including sensing, haptics, orthotics and robotics. The result is a device that helps lifting the forearm by means of a passive exoskeleton and improves the grasping ability of the impaired hand by employing a wearable robotic supernumerary finger. A pilot study involving 3 patients, which was conducted to test the capability of the device to assist in performing ADLs, confirmed its usefulness and serves as a first step in the investigation of novel paradigms for robotic assistance.

Living beings modulate the impedance of their joints to interact proficiently, robustly, and safely with the environment. These observations inspired the design of soft articulated robots with the development of Variable Impedance and Variable Stiffness Actuators. However, designing them remains a challenging task due to their mechanical complexity, encumbrance, and weight, but also due to the different specifications that the wide range of applications requires. For instance, as prostheses or parts of humanoid systems, there is currently a need for multi-degree-of-freedom joints that have abilities similar to those of human articulations. Toward this goal, we propose a new compact and configurable design for a two-degree-of-freedom variable stiffness joint that can match the passive behavior of a human wrist and ankle. Using only three motors, this joint can control its equilibrium orientation around two perpendicular axes and its overall stiffness as a one-dimensional parameter, like the co-contraction of human muscles. The kinematic architecture builds upon a state-of-the-art rigid parallel mechanism with the addition of nonlinear elastic elements to allow the control of the stiffness. The mechanical parameters of the proposed system can be optimized to match desired passive compliant behaviors and to fit various applications (e.g., prosthetic wrists or ankles, artificial wrists, etc.). After describing the joint structure, we detail the kinetostatic analysis to derive the compliant behavior as a function of the design parameters and to prove the variable stiffness ability of the system. Besides, we provide sets of design parameters to match the passive compliance of either a human wrist or ankle. Moreover, to show the versatility of the proposed joint architecture and as guidelines for the future designer, we describe the influence of the main design parameters on the system stiffness characteristic and show the potential of the design for more complex applications.

Force feedback is often beneficial for robotic teleoperation, as it enhances the user’s remote perception. Over the years, many kinesthetic haptic displays (KHDs) have been proposed for this purpose, which have different types of interaction and feedback, depending on their kinematics and their interface with the operator, including, for example, grounded and wearable devices acting either at the joint or operational space (OS) level. Most KHDs in the literature are for the upper limb, with a majority acting at the shoulder/elbow level, and others focusing on hand movements. A minority exists which addresses wrist motions. In this paper, we present the Wearable Delta (W$ \Delta $), a proof-of-concept wearable wrist interface with hybrid parallel–serial kinematics acting in the OS, able to render a desired force directly to the hand involving just the forearm–hand subsystem. It has six degrees of freedom (DoFs), three of which are actuated, and is designed to reduce the obstruction of the range of the user’s wrist. Integrated with positions/inertial sensors at the elbow and upper arm, the W$ \Delta $ allows the remote control of a full articulated robotic arm. The paper covers the whole designing process, from the concept to the validation, as well as a multisubject experimental campaign that investigates its usability. Finally, it presents a section that, starting from the experimental results, aims to discuss and summarize the W$ \Delta $ advantages and limitations and look for possible future improvements and research directions.

Recently, extratheses, aka Supernumerary Robotic Limbs (SRLs), are emerging as a new trend in the field of assistive and rehabilitation devices. We proposed the SoftHand X, a system composed of an anthropomorphic soft hand extrathesis, with a gravity support boom and a control interface for the patient. In preliminary tests, the system exhibited a positive outlook toward assisting impaired people during daily life activities and fighting learned-non-use of the impaired arm. However, similar to many robot-aided therapies, the use of the system may induce side effects that can be detrimental and worsen patients' conditions. One of the most common is the onset of alternative grasping strategies and compensatory movements, which clinicians absolutely need to counter in physical therapy. Before embarking in systematic experimentation with the SoftHand X on patients, it is essential that the system is demonstrated not to lead to an increase of compensation habits. This paper provides a detailed description of the compensatory movements performed by healthy subjects using the SoftHand X. Eleven right-handed healthy subjects were involved within an experimental protocol in which kinematic data of the upper body and EMG signals of the arm were acquired. Each subject executed tasks with and without the robotic system, considering this last situation as reference of optimal behavior. A comparison between two different configurations of the robotic hand was performed to understand if this aspect may affect the compensatory movements. Results demonstrated that the use of the apparatus reduces the range of motion of the wrist, elbow and shoulder, while it increases the range of the trunk and head movements. On the other hand, EMG analysis indicated that muscle activation was very similar among all the conditions. Results obtained suggest that the system may be used as assistive device without causing an over-use of the arm joints, and opens the way to clinical trials with patients.

In the execution of repetitive tasks, humans can capitalize on experience to improve their motor performance. Prominent examples of this ability can be recognized in our capacity of grasping and manipulating in uncertain conditions. With the aim of providing a mathematical description for such behavior, experiments are considered where participants are required to lift an object with an unexpected mass distribution. By repeating multiple times the same lifting action, participants can learn the correct motor command for task accomplishment. Three models are proposed that combine reactive terms and a learned anticipatory action to explain experimental data. The models feature intratrial and intertrial memory, and the effect of slowly and fast adaptive sensory receptors. The architectures’ effectiveness in explaining experimental data is compared with a general‐purpose state of the art model. The proposed algorithms conspicuously outperform the state of the art in all the considered validation routines. Global and within‐trial human behavior is predicted with 88% of accuracy in nominal conditions. When the object's center of mass is moved, the accuracy is maintained up to 83%. Finally, convergence properties of proposed algorithms are analytically discussed, and their stability and robustness against measurement noise are evaluated in simulation.

Human beings can achieve a high level of motor performance that is still unmatched in robotic systems. These capabilities can be ascribed to two main enabling factors: (i) the physical proprieties of human musculoskeletal system, and (ii) the effectiveness of the control operated by the central nervous system. Regarding point (i), the introduction of compliant elements in the robotic structure can be regarded as an attempt to bridge the gap between the animal body and the robot one. Soft articulated robots aim at replicating the musculoskeletal characteristics of vertebrates. Yet, substantial advancements are still needed under a control point of view, to fully exploit the new possibilities provided by soft robotic bodies. This paper introduces a control framework that ensures natural movements in articulated soft robots, implementing specific functionalities of the human central nervous system, i.e., learning by repetition, after-effect on known and unknown trajectories, anticipatory behavior, its reactive re-planning, and state covariation in precise task execution. The control architecture we propose has a hierarchical structure composed of two levels. The low level deals with dynamic inversion and focuses on trajectory tracking problems. The high level manages the degree of freedom redundancy, and it allows to control the system through a reduced set of variables. The building blocks of this novel control architecture are well-rooted in the control theory, which can furnish an established vocabulary to describe the functional mechanisms underlying the motor control system. The proposed control architecture is validated through simulations and experiments on a bio-mimetic articulated soft robot.

To physically interact with a rich variety of environments and to match situation-dependent requirements, humans adapt both the force and stiffness of their limbs. Reflecting this behavior in prostheses may promote a more natural and intuitive control and, consequently, improve prostheses acceptance in everyday life. This pilot study proposes a method to control a prosthetic robot hand and its impedance, and explores the utility of variable stiffness when performing activities of daily living and physical social interactions. The proposed method is capable of a simultaneous and proportional decoding of position and stiffness intentions from two surface electro-myographic sensors placed over a pair of antagonistic muscles. The feasibility of our approach is validated and compared to existing control modalities in a preliminary study involving one prosthesis user. The algorithm is implemented in a soft under-actuated prosthetic hand (SoftHand Pro). Then, we evaluate the usability of the proposed approach while executing a variety of tasks. Among these tasks, the user interacts with other 12 able-bodied subjects, whose experiences were also assessed. Several statistically significant aspects from the System Usability Scale indicate user's preference of variable stiffness control over low or high constant stiffness due to its reactivity and adaptability. Feedback reported by able-bodied subjects reveal a general tendency to favor soft interaction, i.e., low stiffness, which is perceived more human-like and comfortable. These combined results suggest the use of variable stiffness as a viable compromise between firm control and safe interaction which is worth investigating further.

Abstract Background Roughly one-quarter of upper limb prosthesis users reject their prosthesis. Reasons for rejection range from comfort, to cost, aesthetics, function, and more. This paper follows a single user from training with and testing of a novel upper-limb myoelectric prosthesis (the SoftHand Pro) for participation in the CYBATHLON rehearsal to training for and competing in the CYBATHLON 2016 with a figure-of-nine harness controlled powered prosthesis (SoftHand Pro-H) to explore the feasibility and usability of a flexible anthropomorphic prosthetic hand. Methods The CYBATHLON pilot took part in multiple in-lab training sessions with the SoftHand Pro and SoftHand Pro-H; these sessions focused on basic control and use of the prosthetic devices and direct training of the tasks in the CYBATHLON. He used these devices in competition in the Powered Arm Prosthesis Race in the CYBATHLON rehearsal and 2016 events. Results In training for the CYBATHLON rehearsal, the subject was able to quickly improve performance with the myoelectric SHP despite typically using a body-powered prosthetic hook. The subject improved further with additional training using the figure-of-nine harness-controlled SHPH in preparation for the CYBATHLON. The Pilot placed 3rd (out of 4) in the rehearsal. In the CYBATHLON, he placed 5th (out of 12) and was one of only two pilots who successfully completed all tasks in the competition, having the second-highest score overall. Conclusions Results with the SoftHand Pro and Pro-H suggest it to be a viable alternative to existing anthropomorphic hands and show that the unique flexibility of the hand is easily learned and exploited.

The size, weight, and power consumption of soft wearable robots rapidly scale with their number of active degrees of freedom. While various underactuation strategies have been proposed, most of them impose hard constrains on the kinetics and kinematics of the device. Here we propose a paradigm to independently control multiple degrees of freedom using a set of modular components, all tapping power from a single motor. Each module consists of three electromagnetic clutches, controlled to convert a constant unidirectional motion in an arbitrary output trajectory. We detail the design and functioning principle of each module and propose an approach to control the velocity and position of its output. The device is characterized in free space and under loading conditions. Finally, we test the performance of the proposed actuation scheme to drive a soft exosuit for the elbow joint, comparing it with the performance obtained using a traditional DC motor and an unpowered-exosuit condition. The exosuit powered by our novel scheme reduces the biological torque required to move by an average of 46.2%, compared to the unpowered condition, but negatively affects movement smoothness. When compared to a DC motor, using the our paradigm slightly deteriorates performance. Despite the technical limitations of the current design, the method proposed in this paper is a promising way to design more portable wearable robots.

Inspired by nature, soft robotics aims at enhancing robots capabilities through the use of soft materials. This article presents the study of soft continuum robots which can change their dynamic behavior thanks to a proper design of their damping properties. It enables an under-actuated dynamic strategy to control multi-chamber pneumatic systems using a reduced number of feeding lines. The present work starts from the conceptual investigation of a way to tune the damping properties of soft continuum robots, and leverages on the introduction of viscous fluid within the soft chamber wall to produce dissipative actions. Several solutions are analyzed in simulations and the most promising one is tested experimentally. The proposed approach employs a layer of granular material immersed in viscous silicone oil to increase the damping effect. After validation and experimental characterization, the method is employed to build soft continuum actuators with different deformation patterns, i.e., extending, contracting and bending. Experimental results show the dynamic behavior of the presented actuators. Finally, the work reports information on how the actuators are designed and builded, together with a discussion about possible applications and uses.

Humans are capable of complex manipulation interactions with the environment, relying on the intrinsic adaptability and compliance of their hands. Recently, soft robotic manipulation has attempted to reproduce such an extraordinary behavior, through the design of deformable yet robust end-effectors. To this goal, the investigation of human behavior has become crucial to correctly inform technological developments of robotic hands that can successfully exploit environmental constraint as humans actually do. Among the different tools robotics can leverage on to achieve this objective, deep learning has emerged as a promising approach for the study and then the implementation of neuro-scientific observations on the artificial side. However, current approaches tend to neglect the dynamic nature of hand pose recognition problems, limiting the effectiveness of these techniques in identifying sequences of manipulation primitives underpinning action generation, e.g., during purposeful interaction with the environment. In this work, we propose a vision-based supervised Hand Pose Recognition method which, for the first time, takes into account temporal information to identify meaningful sequences of actions in grasping and manipulation tasks. More specifically, we apply Deep Neural Networks to automatically learn features from hand posture images that consist of frames extracted from grasping and manipulation task videos with objects and external environmental constraints. For training purposes, videos are divided into intervals, each associated to a specific action by a human supervisor. The proposed algorithm combines a Convolutional Neural Network to detect the hand within each video frame and a Recurrent Neural Network to predict the hand action in the current frame, while taking into consideration the history of actions performed in the previous frames. Experimental validation has been performed on two datasets of dynamic hand-centric strategies, where subjects regularly interact with objects and environment. Proposed architecture achieved a very good classification accuracy on both datasets, reaching performance up to 94%, and outperforming state of the art techniques. The outcomes of this study can be successfully applied to robotics, e.g., for planning and control of soft anthropomorphic manipulators.

Roughly one quarter of active upper limb prosthetic technology is rejected by the user, and user surveys have identified key areas requiring improvement: function, comfort, cost, durability, and appearance. Here we present the first systematic, clinical assessment of a novel prosthetic hand, the SoftHand Pro (SHP), in participants with transradial amputation and age-matched, limb-intact participants. The SHP is a robust and functional prosthetic hand that minimizes cost and weight using an underactuated design with a single motor. Participants with limb loss were evaluated on functional clinical measures before and after a 6-8 hour training period with the SHP as well as with their own prosthesis; limb-intact participants were tested only before and after SHP training. Participants with limb loss also evaluated their own prosthesis and the SHP (following training) using subjective questionnaires. Both objective and subjective results were positive and illuminated the strengths and weaknesses of the SHP. In particular, results pre-training show the SHP is easy to use, and significant improvement in the Activities Measure for Upper Limb Amputees in both groups following a 6-8 hour training highlights the ease of learning the unique features of the SHP (median improvement: 4.71 and 3.26 and p = 0.009 and 0.036 for limb loss and limb-intact groups, respectively). Further, we found no difference in performance compared to participant's own commercial devices in several clinical measures and found performance surpassing these devices on two functional tasks, buttoning a shirt and using a cell phone, suggesting a functional prosthetic design. Finally, improvements are needed in the SHP design and/or training in light of poor results in small object manipulation. Taken together, these results show the promise of the SHP, a flexible and adaptive prosthetic hand, and pave a path forward to ensuring higher functionality in future.

The rich variety of human upper limb movements requires an extraordinary coordination of different joints according to specific spatio-temporal patterns. However, unvealing these motor schemes is a challenging task. Principal components have been often used for analogous purposes, but such an approach relies on hypothesis of temporal uncorrelation of upper limb poses in time. To overcome these limitations, in this work, we leverage on functional principal component analysis (fPCA). We carried out experiments with 7 subjects performing a set of most significant human actions, selected considering state-of-the-art grasp taxonomies and human kinematic workspace. fPCA results show that human upper limb trajectories can be reconstructed by a linear combination of few principal time-dependent functions, with a first component alone explaining around 60/70% of the observed behaviors. This allows to infer that in daily living activities humans reduce the complexity of movement by modulating their motions through a reduced set of few principal patterns. Finally, we discuss how this approach could be profitably applied in robotics and bioengineering, opening fascinating perspectives to advance the state of the art of artificial systems, as it was the case of hand synergies.

Humans are able to intuitively exploit the shape of an object and environmental constraints to achieve stable grasps and perform dexterous manipulations. In doing that, a vast range of kinematic strategies can be observed. However, in this work we formulate the hypothesis that such ability can be described in terms of a synergistic behavior in the generation of hand postures, i.e., using a reduced set of commonly used kinematic patterns. This is in analogy with previous studies showing the presence of such behavior in different tasks, such as grasping. We investigated this hypothesis in experiments performed by six subjects, who were asked to grasp objects from a flat surface. We quantitatively characterized hand posture behavior from a kinematic perspective, i.e., the hand joint angles, in both pre-shaping and during the interaction with the environment. To determine the role of tactile feedback, we repeated the same experiments but with subjects wearing a rigid shell on the fingertips to reduce cutaneous afferent inputs. Results show the persistence of at least two postural synergies in all the considered experimental conditions and phases. Tactile impairment does not alter significantly the first two synergies, and contact with the environment generates a change only for higher order Principal Components. A good match also arises between the first synergy found in our analysis and the first synergy of grasping as quantified by previous work. The present study is motivated by the interest of learning from the human example, extracting lessons that can be applied in robot design and control. Thus, we conclude with a discussion on implications for robotics of our findings.

Myoelectric-artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human-likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed such as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an under-actuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e. flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step towards a more effective and intuitive control of myoelectric hand prostheses.

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses.

This paper presents an analysis of planar bearing localization and mapping for visual servoing with known camera velocities. In particular, we investigate what is the subset of camera locations and environmental features that can be retrieved from dynamic observations obtained by a planar bearing sensor (nearly e.g., a pinhole camera). Results assume that the camera's linear and angular velocities are available, which is equivalent to consider a unicycle vehicle carrying an onboard camera. Results hold if other system inputs are considered, e.g., an omnidirectional vehicle. The theoretical results may guide the design of nonlinear observers to estimate the variables of interest in real time to be applied to visual servoing schemes. An example of such an observer is discussed and simulated.

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.