Hugs are complex affective interactions that often include gestures like squeezes. We present six new guidelines for designing interactive hugging robots, which we validate through two studies with our custom robot. To achieve autonomy, we investigated robot responses to four human intra-hug gestures: holding, rubbing, patting, and squeezing. A Total of 32 users each exchanged and rated 16 hugs with an experimenter-controlled HuggieBot 2.0. The robot’s inflated torso’s microphone and pressure sensor collected data of the subjects’ demonstrations that were used to develop a perceptual algorithm that classifies user actions with 88% accuracy. Users enjoyed robot squeezes, regardless of their performed action, they valued variety in the robot response, and they appreciated robot-initiated intra-hug gestures. From average user ratings, we created a probabilistic behavior algorithm that chooses robot responses in real time. We implemented improvements to the robot platform to create HuggieBot 3.0 and then validated its gesture perception system and behavior algorithm with 16 users. The robot’s responses and proactive gestures were greatly enjoyed. Users found the robot more natural, enjoyable, and intelligent in the last phase of the experiment than in the first. After the study, they felt more understood by the robot and thought robots were nicer to hug.

Receiving a hug is one of the best ways to feel socially supported, and the lack of social touch can have severe negative effects on an individual's well-being. Based on previous research both within and outside of HRI, we propose six tenets ("commandments") of natural and enjoyable robotic hugging: a hugging robot should be soft, be warm, be human sized, visually perceive its user, adjust its embrace to the user's size and position, and reliably release when the user wants to end the hug. Prior work validated the first two tenets, and the final four are new. We followed all six tenets to create a new robotic platform, HuggieBot 2.0, that has a soft, warm, inflated body (HuggieChest) and uses visual and haptic sensing to deliver closed-loop hugging. We first verified the outward appeal of this platform in comparison to the previous PR2-based HuggieBot 1.0 via an online video-watching study involving 117 users. We then conducted an in-person experiment in which 32 users each exchanged eight hugs with HuggieBot 2.0, experiencing all combinations of visual hug initiation, haptic sizing, and haptic releasing. The results show that adding haptic reactivity definitively improves user perception a hugging robot, largely verifying our four new tenets and illuminating several interesting opportunities for further improvement.

Among the currently available grasp-type selection techniques for hand prostheses, there is a distinct lack of intuitive, robust, low-latency solutions. In this paper we investigate the use of a portable, forearm-mounted, video-based technique for the prediction of hand-grasp preshaping for arbitrary objects. The purpose of this system is to automatically select the grasp-type for the user of the prosthesis, potentially increasing ease-of-use and functionality. This system can be used to supplement and improve existing control strategies, such as surface electromyography (sEMG) pattern recognition, for prosthetic and orthotic devices. We designed and created a suitable dataset consisting of RGB-D video data for 2212 grasp examples split evenly across 7 classes; 6 grasps commonly used in activities of daily living, and an additional no-grasp category. We processed and analyzed the dataset using several state-of-the-art deep learning architectures. Our selected model shows promising results for realistic, intuitive, real-world use, reaching per-frame accuracies on video sequences of up to 95.90% on the validation set. Such a system could be integrated into the palm of a hand prosthesis, allowing an automatic prediction of the grasp-type without requiring any special movements or aiming by the user.

The past decade has seen an increasing number of motor control studies using functional magnetic resonance imaging to explore the underlying neural mechanisms. To enable advanced experimental paradigms under well-controlled and reproducible conditions, a number of fMRI-compatible haptic interfaces have been developed for such studies. These devices typically rely on actuation principles and transmissions with non-linear behavior and large output impedance. Fiberoptic force sensors are a common and well-established means of measuring interaction forces with subjects or to reduce the inherent dynamics of the haptic interface through force feedback. The elastic probes for such sensors are typically fabricated by expensive methods such as electro-erosion of non-ferromagnetic metals or injection moulding of polymers, or are realized by milling of polymers, resulting in bulky structures. In this paper we propose a compact and integrated elastic probe for fiberoptics-based force sensing, developed using low-cost off-the-shelf 3D printing technology. Characterization of the sensor probe shows high linearity, repeatability and temporal stability, as well as high reproducibility in terms of the manufacturing process. The realized sensor is integrated into a linear grasper to evaluate its performance in force-feedback applications, underlining the potential of this technology for use in fMRI-compatible haptic interfaces.

The paper describes a motion planning and control software architecture developed for the automation of a surgical robot. The considered surgical robot is a dual-arm prototype developed with a redundant and modular mechanical structure, designed to be reconfigured for different surgical tasks, and with a hybrid parallel/serial kinematics. The motion planning solution proposed in the paper includes both an online collision-free path planner, based on the RRT-Connect algorithm, and a generator of predefined motion primitives. This solution allows the multi-arm robot to autonomously execute the complex motion patterns required for a suturing task. Since such motion patterns are specified in the Cartesian space, an efficient and univocal solution of the inverse kinematics of the robot, which is a challenging problem due to its hybrid structure, is another crucial issue addressed in the paper.

Introducing some form of autonomy in robotic surgery is being considered by the medical community to better exploit the potential of robots in the operating room. However, significant technological steps have to occur before even the smallest autonomous task is ready to be presented to the regulatory authorities. In this paper, we address the initial steps of this process, in particular the development of control concepts satisfying the basic safety requirements of robotic surgery, i.e., providing the robot with the necessary dexterity and a stable and smooth behavior of the surgical tool. Two specific situations are considered: the automatic adaptation to changing tissue stiffness and the transition from autonomous to teleoperated mode. These situations replicate real-life cases when the surgeon adapts the stiffness of her/his arm to penetrate tissues of different consistency and when, due to an unexpected event, the surgeon has to take over the control of the surgical robot. To address the first case, we propose a passivity-based interactive control architecture that allows us to implement stable time-varying interactive behaviors. For the second case, we present a two-layered bilateral control architecture that ensures a stable behavior during the transition between autonomy and teleoperation and, after the switch, limits the effect of initial mismatch between master and slave poses. The proposed solutions are validated in the realistic surgical scenario developed within the EU-funded I-SUR project, using a surgical robot prototype specifically designed for the autonomous execution of surgical tasks like the insertion of needles into the human body.

Sensory function is essential for functional post-stroke recovery and control of basic hand movements like grasping. Despite this fact, therapy focuses strongly on motor aspects of rehabilitation, requiring active participation and thus excluding stroke patients with severe paresis. The aim of our novel therapeutic approach combining virtual reality, based on clinically proven mirror therapy, and tendon vibration of hand and wrist muscles is to induce neuroplastic changes leading to improved hand function. This paper presents the further development and evaluation of a robotic device, which can apply vibrations at precise locations on the finger flexor tendons to create illusions of extension movements and visualize the movements with a virtual hand. A preliminary study including 16 healthy subjects investigated the influence of the virtual reality on the perception of proprioceptive illusory movements. The experimental results provided evidence that the addition of the virtual reality enhanced the perception of the illusory movement generated by tendon vibration, by inducing movements with significantly higher extension (+4.5%, p <; 0.05). Furthermore, the virtual reality allowed a better controlled temporal elicitation of the illusion. These findings indicate the potential of this novel strategy for a more effective therapy, especially for severely impaired patients.

Stroke is the leading cause of long-term disability in adults, with increasing incidence as the population ages. Physical and occupational therapy support the recovery process, with therapy duration and intensity having been identified as critical factors. However, conventional therapy is limited to a short period following the stroke, with only limited possibilities to continue therapy at home. Increasing health costs and lack of trained personnel further strengthen this problem. There is thus a great need for home-based therapy systems, allowing to engage patients and assess the training with sufficient precision to provide meaningful exercises. In this paper, we suggest expandable software architecture for virtual reality rehabilitation applications. This system utilizes various sensors and actuators (i.e. the Kinect for upper limb tracking and a haptic glove). Based on this system, we propose a motion based tactile rendering algorithm. It generates interactive vibration patterns employing the tactile apparent movement phenomenon based on user's movement to enhance the immersion and provide sensory feedback during rehabilitation training in the virtual reality environment. Three rehabilitation game applications are presented as proof of concept of the proposed system.

In this paper, a new hand exoskeleton device using a three-layered sliding spring mechanism is presented. In contrast to state of the art hand exoskeleton mechanisms (typically link, wire or pneumatically driven), the proposed mechanism is driven through large deformations of the compliant mechanism body. The mechanism can be made compact and lightweight by adequately positioning the compliant elements. In addition, the mechanism is designed to distribute 1-DOF actuated linear motion into three rotational motions of the finger joints, which translate into natural finger flexion/extension. The primary application of the proposed mechanism is to provide robotic support during physical therapy at the hospital (e.g. Continuous Passive Motion). However, thanks to its light and wearable structure, the proposed device could also be used at home as an assistive/therapeutic device to support activities of daily living. We introduce the mechanical structure of the three-layered sliding spring mechanism, present a prototype implementation as a hand exoskeleton device, and provide a preliminary evaluation.

Robot-assisted rehabilitation can complement conventional rehabilitation after stroke, by increasing the duration and intensity of therapy and providing precise and objective measurements of interaction dynamics and performance. Such information can be used to drive assist-as-needed control strategies or to complement clinical assessments by reconstructing the scores from robot data. This paper presents the ReHapticKnob, a new end-effector-based hand rehabilitation robot with unique sensing and actuation capabilities for therapy of grasping and forearm rotation tasks. A compact design with high stiffness and high-fidelity instrumentation is presented, allowing for precise assessment and dynamic interaction. The device has two degrees of freedom (DOF), allowing independent control of hand opening/closing and forearm rotation. Each degree of freedom is equipped with a brake to allow independent training of either DOF or to assess isometric force through the two six-axis force/torque sensors located beneath the exchangeable finger fixations. The design, safety features and performance evaluation of the device are discussed and preliminary results from a study on grasping performed with healthy subjects are presented.

The neural mechanisms of fine motor control and recovery, e.g. after a stroke, are not fully understood, nor how these are influenced by different types of motor therapies, leaving potential for optimization of current rehabilitation strategies. This paper presents the development and evaluation of a novel robotic tool for fMRI-based neuroscience studies allowing to investigate the neural mechanisms of dynamic precision grip and track therapy-induced neuroplasticity. In this proof of principle study we investigate the feasibility of high-fidelity haptic interaction with human motion using remote sensing and actuation. A cable-spring mechanism transmits force to the thumb and index finger in an unconstrained manner, actuated over a stiff cable transmission. Characterization of the prototype with a transmission length of two meters revealed good dynamic performance including a 16 Hz open loop force bandwidth and a maximal output force of 28 N. Combined with a remote and shielded conventional electromagnetic actuator, this device could be used to investigate the neural correlates of precision grasping as well as the effect of different hand function therapies on the neural correlates of motor recovery after stroke.

Haptic interfaces compatible with functional magnetic resonance imaging (fMRI) are finding increasing interest as a tool to explore the neural correlates of human motor control and related dysfunctions. To achieve safety and MR compatibility, such devices have mainly relied on unconventional actuation methods suffering from limited bandwidth and non-linearities. This has resulted in complex control and restricted their use in applications involving fine and dynamic interaction with the hand and fingers. To address these limitations, we propose a concept for a shielded high-performance actuation system to be located inside the MR room, evaluate the effectiveness of the shielding and perform detailed MR compatibility tests. A conventional electromagnetic actuator is located within a steel shield to prevent mutual disturbance with magnetic fields of the scanner, which, together with power and control hardware, is placed within a Faraday cage with only a fiber-optical USB link to the control room. Detailed compatibility tests show that disturbing dynamic electromagnetic fields generated by the actuation system are well below the detectable threshold of the scanner, and actuator performance is not degraded by the MR environment. In combination with a light and stiff cable or rod transmission, the presented actuator technology, providing high transparency and force bandwidth, paves the way for fMRI-based neuroscience studies, e.g., to investigate the fine motor control of hand and fingers.

During traditional open procedures, surgeons directly palpate tissues before dissecting them. In this way, they can avoid the accidental damage of hidden arteries that can lead to fatal hemorrhage. New Minimally Invasive Surgical (MIS) techniques progressively decreased the instrument access into the patient's body to reduce scars and side effects. The major drawback of these procedures is that they do not permit surgeons to perform direct tactile exploration of internal tissues. Surgeons have to rely on preoperative images and anatomical knowledge to avoid artery locations. However, the exact artery position changes depending on the patient and his posture. Hence, it is of primary importance to assist surgeons with technology that can guide them during the surgical procedure. This paper presents the design and evaluation of a tactile display that reproduces pulse-like feedback on the surgeon's fingertip. The display bandwidth and performance of the ad-hoc control unit were assessed with encouraging results. In addition, the outcome of two psychophysical studies carried out in this work validate the usability of the display in terms of user perception.

The systems which have been developed are safe, easily transportable and offer various training possibilities. Virtual-reality games have been implemented to increase motivation during the 8 weeks of training with post-stroke subjects. Clinical trials have shown that chronic stroke patients can perform the required tasks and that training improves their motor function.

This paper describes the development of a 2-DOF electrostatic haptic joystick designed for neuroscience studies in an MRI/fMRI. The joystick is fabricated using non-magnetic materials and actuated by two high-power electrostatic motor units which produce various force fields in the horizontal plane. The electrostatic motor is a synchronous drive and thus the positioning of the joystick is achieved in an open-loop control. As for force sensing, a 2-DOF force sensor is developed using non-magnetic materials and optical fibers so as to measure interaction force with a user; hence, the haptic rendering is based on the admittance control scheme that respects the rule of force-in and position-out. The operation of both the actuators and force sensor respects non-magnetic principles. Thus, the haptic joystick into which these components are integrated is expected to have high MR compatibility, although the evaluation of the MR compatibility is beyond the scope of this paper. In this paper, the device performance is evaluated in the normal environment, which verifies the operation of the unique electrostatic haptic device.

Robotic devices able to train both reaching and manipulation are often large and complex and thus not suitable for decentralized use at home or in local rehabilitation centers. This paper describes a compact device with only three degrees of freedom (DOF) to train reaching and manipulation critical to activities of daily living. The design considers only the DOF necessary to train tasks such as pick-and-place of objects, drinking, eating and knob manipulation, based on low-dimensional synergies used in these tasks. Specifications from measured biomechanical parameters yield safety and suitable performance. A prototype demonstrates some of the resulting functions and therapeutic possibilities offered by this design.

This paper describes a two-degrees-of-freedom haptic interface to investigate the brain mechanisms of human motor control, which is capable of safely and gently interacting with human arm motion during functional magnetic resonance imaging (fMRI). A hydrostatic transmission separates the interface into a master and an MR compatible slave system, allowing the placement of all interfering components outside the electromagnetic shield of the MR room. The transmission mirrors force and motion of the master actuators on the slave system placed close to the MR scanner. The parallel architecture takes advantage of the linear MR compatible actuators and allows human subjects to perform reaching movements comfortably in the small workspace limited by the dimensions of the MR scanner and the biomechanics of the arm. The kinematic structure of the slave interface was optimized with respect to the available space and types of movements to be investigated. Materials were chosen based on their MR compatibility, their stiffness and weight. The interaction force with the subject is measured over two optical force sensors, located close to the output of the interface. Two shielded optoelectronic encoders measure the extension of the slave hydraulic pistons. Detailed tests demonstrated the fMRI compatibility even during movement of the interface

Functional brain exploration methodologies such as functional magnetic resonance imaging (fMRI) are critical tools to study perceptual and cognitive processes. In order to develop complex and well-controlled fMRI paradigms, researchers are interested in using active interfaces with electrically powered actuators and sensors. Due to the particularity of the MR environment, safety and compatibility criteria have to be strictly followed to avoid risks to the subject under test, the operators or the environment, as well as to prevent artifacts in the images. This paper describes the design of an fMRI compatible mechatronic interface based on MR compatibility tests of materials and actuators. In particular, a statistical test is introduced to evaluate the presence of artifacts in the image sequences that could negatively affect the fMRI studies. The device with two degrees of freedom, allowing one translation with position-feedback along a horizontal axis and one rotation about a vertical axis linked to the translation, was realized to investigate the brain mechanisms of dynamic tactile perception tasks. It can be used to move and orient various objects below the finger for controlled tactile stimulation. The MR compatibility of the complete interface is shown using the statistical test as well as a functional study with a human subject

Using haptic interfaces in combination with functional magnetic resonance imaging (fMRI) could lead to important insights into the brain mechanisms of human motor control and related dysfunctions. However, in addition to the usual requirements for haptic interfaces (e.g. smooth force control, back-drivability, low friction and inertia) these devices must also be MR safe and MR compatible. Previous MR-compatible actuation methods for force-feedback present drawbacks with respect to conventional haptic interfaces. Here, we present a novel MR-compatible actuator designed especially for impedance control and to meet the requirements for haptic interfaces. It consists of an ultrasonic motor controlled in speed combined with an electrorheological fluid brake which modulates the output torque over a differential gear. The entire system is integrated into a compact housing with an encoder at the output

The strong impairment of motor functions in stroke survivors affects daily activities such as eating, manipulating objects or writing. Our goal is to induce long lasting improvements in such tasks by having patients perform systematic exercises using haptic interfaces. This paper describes a novel two-degrees-of-freedom interface which we have developed to help stroke patients gradually recover the ability to open and close the hand and manipulate knobs. Different solutions are studied and a design consisting of two parallelogram structures interacting with the fingers is proposed. The mechanical design offers the possibility to adapt the interface to various hand sizes and finger orientations, and to right or left-handed subjects. Design kinematics as well as actuation and system control are described. Several knobs are proposed to interact with patients, especially a cone mechanism to train a complete opening movement from a strongly contracted and closed hand to a large opened position. The interaction force with the subject is measured over four force sensors located close to the output of the interface. A preliminary study has been performed to evaluate the performances of the haptic interface

This paper reports a new MRI (Magnetic Resonance Imaging) compatible actuator that can operate near an MRI without degrading its imaging quality. A high-power electrostatic motor is introduced and its MR-compatibility is examined, both experimentally and theoretically. The proposed motor was fabricated with only paramagnetic materials, and its MR-compatibility was evaluated by measuring the output performance of the motor and the signal-to-noise ratios of the MRI images. The experimental results showed that motor’s thrust force while positioned within the MRI is almost the same as that in normal operation. The signal-to-noise investigation indicated that the motor did not affect the MRI images when it was operated over 60 cm away from the MRI’s RF coil. The theoretical calculations showed that the magnetic field generated by the motor is relatively small and the effect of the MRI’s magnetic fields on the motor is negligible, suggesting that the motor is MR-compatible.

We analyze the dynamics of an MR-compatible hydrostatic transmission designed to transfer power over distances of up to 10 m. In this system, a master actuates a passive slave connected by two hydrostatic lines in a cyclic arrangement. We derive a nonlinear model of this system and use it to analyze the system's behavior and the design parameters. The transmission acts as a low-pass filter with cut-off frequency decreasing for longer hoses. Even for a length of 10 m the cut-off frequency is about 20 Hz, resulting in a bandwidth that suffices for haptic interfaces interacting with human motion as well as for medical robots. A pragmatic control delivered free movements, position and velocity dependent force fields and trajectory control suitable to investigate how the brain controls movements in interaction with the environment. For short hose lengths (/spl les/1 m) the dynamics can be well approximated by a linear model, and the system is dynamic and stiff. The hydraulic transmission can produce force and motion in any orientation, enabling a more flexible design than other types of transmissions such as by cables.

Force/torque sensors compatible with magnetic resonance imaging (MRI) are required to develop haptic interfaces for neuroscientific investigations and robotic tools for interventional MRI. In this paper, we analyze the mechanical structure of classical and MRI compatible sensors from literature and demonstrate the critical role of the mechanical design on the sensing performance. A simple and efficient torque sensor based on light intensity measurement over optical fibers is introduced, which allows to place the electronic components outside the scanner room. By using a self-guiding flexible structure and optimal mirror placement, the sensitivity to transverse torque is reduced to 0.03% of the desired output torque.

Magnetically compatible robots are required to develop haptic interfaces for neuroscience studies and MRI guided robots for minimally invasive interventions. This paper introduces an MR compatible master slave concept using a hydraulic transmission, and presents an operational robot able to work within an MRI/fMRI scanner and acquire images continuously during motion. It describes a magnetically inert actuator using a direct drive to power the hydraulic circuitry and a modular set of position and force/torque sensors that we have developed. These were integrated into a haptic interface prototype with on rotary degree of freedom, which can be used in conjuction with an fMRI. The MR compatibility was confirmed experimentally, and the performances show a manipulation accuracy of a few micrometers over a range of several centimeters, and forces up to several thousand Newton.