The lack of sensory feedback in lower-limb amputees is the major cause of (i) amputees difficulty to keep balance, (ii) suboptimal performance in gait functions and (iii) increased energy consumption. This also hugely affects their participation in the activities of daily living. Improving postural control functions in people with lower-limb amputation has the aim to enhance their quality of life and is achieved through the restoration of their lost sensory feedback. The objective of this paper is to propose a stimulation system for restoring plantar pressure perception and knee-joint proprioception in lower-limb amputees. The proposed approach is based on the combined use of two FSRs, two accelerometers and two or three vibrotactile actuators. For validating the system, two experimental sessions were carried out on sixteen healthy subjects and one lower-limb amputee. They were aimed to (i) assess if vibrotactile feedback can improve balance control in lower-limb amputees and (ii) investigate the potentiality of the vibrotactile perception as a means to restore amputees' knee-joint proprioception. Quantitative indices capable of describing users' performance were extracted from the processed data and a statistical analysis was performed to compare different types of sensory feedback: i.e. (i) augmented visuo-proprioceptive feedback, no feedback, forearm vibrotactile feedback and low-back vibrotactile feedback in the 1stexperimental session and (ii) forearm continuous vibrotactile feedback, low-back continuous vibrotactile feedback, forearm discrete vibrotactile feedback, and low-back discrete vibrotactile feedback in the 2ndexperimental session. The achieved results on the vibrotactile stimulation were encouraging for both applications, i.e. restoring amputees' plantar pressure perception and knee-joint proprioception.

In this paper we propose and validate a teleoperated control approach for an anthropomorphic redundant robotic manipulator, using magneto-inertial sensors (IMUs). The proposed method allows mapping the motion of the human arm (used as the master) on the robot end-effector (the slave). We record arm movements using IMU sensors, and calculate human forward kinematics to be mapped on robot movements. In order to solve robot kinematic redundancy, we implemented different algorithms for inverse kinematics that allows imposing anthropomorphism criteria on robot movements. The main objective is to let the user to control the robotic platform in an easy and intuitive manner by providing the control input freely moving his/her own arm and exploiting redundancy and anthropomorphism criteria in order to achieve human-like behaviour on the robot arm. Therefore, three inverse kinematics algorithms are implemented: Damped Least Squares (DLS), Elastic Potential (EP) and Augmented Jacobian (AJ). In order to evaluate the performance of the algorithms, four healthy subjects have been asked to control the motion of an anthropomorphic robot arm (i.e. the Kuka Light Weight Robot 4+) through four magneto-inertial sensors (i.e. Xsens Wireless Motion Tracking sensors - MTw) positioned on their arm. Anthropomorphism indices and position and orientation errors between the human hand pose and the robot end-effector pose were evaluated to assess the performance of our approach.

One of the main problems in the prosthetic hand design is to provide the hand with a reliable system for force and slippage control. The real-time detection of an object slippage during grasping plays a fundamental role in performing a stable grasp. Aim of this paper is to propose and test (i) a system for object slippage detection during grasping, (ii) a control strategy for grasping force regulation and slippage prevention and (iii) a finger coordination method for replicating on the prosthesis system a human-like behaviour. The approach has been validated on a real prosthetic hand, i.e. the IH2 Azzurra (developed by Prensilia), on which tips force sensing resistors have been placed. The preliminary experimental results have demonstrated the feasibility of the methods for slipping detection and for force/slippage control and have shown that the prosthetic hand is able to perform a stable pinch grasp of different objects guaranteeing finger coordination.

Rotary Series Elastic Actuators (SEAs) are largely adopted to safely and accurately modulate interaction torques when robots operate in close contact with humans. Torque control is often based on a cascade scheme including PI regulators for a velocity controller nested in a torque controller. This solution is simple, robust and can potentially guarantee coupled stability. A high-level impedance control loop is also commonly added to regulate the behavior of the interaction port as a desired virtual viscoelastic body. In the present work, passivity is analyzed when a cascade controlled SEA is employed to display a virtual parallel spring-damper system. The case of a null desired impedance and of a pure spring are also tackled. The range of renderable mechanical impedance and guidelines for the selection of the control gains are derived.

In cyclic motions, e.g. during locomotion, part of total kinetic energy, which otherwise would be wasted during direction inversion, can be conveniently stored in elastic elements in order to be later reused. Such energy recovery strategy, which can be adopted in robotic systems to improve energetic autonomy, mimics biological solutions, where muscles, tendons and ligaments are exploited also as elastic energy buffers. The addition of compliant elements, in parallel or in series to conventional actuators, may result in complex assemblies. Conversely, compact solutions can be obtained if elasticity is embedded in the structure of the actuator. In this paper an electric rotary compliant actuator, integrating a magnetic non-linear torsion spring, is presented. The system is a parallel elastic actuator and it allows to efficiently produce oscillating motions, as needed in several biorobotic applications. The stator comprises mono-phase windings while the rotor includes permanent magnets in axial Halbach configuration. Additional magnets are advantageously located in the stator to embody the intrinsic parallel elasticity. A proof-of-concept prototype was developed, whose winding coils were designed based on magneto-static FEM simulations. The actuator was experimentally characterized and the present dynamic behavior was compared to the theoretical one calculated by considering a non-linear electro-mechanical model.

This paper presents a robotic system for 3D upperlimb robot-aided rehabilitation, grounded on a patient-tailored approach: it real-time adapts therapy characteristics to patient needs, by including the patient in the control loop. The system is composed of a 7-DoF robot arm, an adaptive interaction control system and a module for the evaluation of patient performance; it records patient biomechanical data through an unobtrusive sensory system, evaluates patient biomechanical state and updates robot control parameters for modifying the level of assistance and task complexity in the 3D space. The adaptive interaction control and the module for assessing patient biomechanical state are described in detail in the paper. The experimental validation is carried out on healthy subjects and results in 2D and 3D space are provided.

One of the main limitations of systems for Minimally Invasive Robotic Surgery is the lack of haptic feedback. In this paper, a teleoperated system for robotic surgery is introduced, able to guide the surgeon towards a target anatomy by providing her with force feedback based on Virtual Fixtures (VF). The teleoperated system has a redundant slave robot. A closed-form inverse kinematics is proposed to solve redundancy that is based on an optimization approach in one variable. Four different cost objective functions are proposed in the paper and one is implemented and validated, i.e. the cost function aimed at minimizing the amount of space of the robot in the operative theater during the surgical procedure. The proposed teleoperated architecture has been tested on a teleoperated system composed of a 3-DoFs haptic joystick and a 7-DoFs anthropomorphic manipulator. Experimental tests on 12 volunteer subjects have been carried out. Results demonstrate that a force feedback based on VF provides a statistically significant enhancement of procedure accuracy.

In complex dynamical tasks human motor control notably exploits the possibility of regulating joints mechanical impedance, both for stability and for energetic optimization purposes. These biomechanical findings should translate in design requirements for wearable robotics joints, which are required to produce adaptable intrinsic viscoelastic behaviors.

It is my great pleasure to introduce you to the Technical Program of the 25th edition of the IEEE\RSJ International Conference on Robotics and Intelligent Systems (IROS 2012).

This paper describes the optimization of a planar wearable active orthosis for hip and knee assistance during overground walking. A non-anthropomorphic design is pursued in order to improve ergonomics and to reduce torque requirements. Based on a previously-developed systematic search algorithm of the admissible generalized solutions for the selected problem, a solution is selected and optimized by means of genetic algorithms and constrained non-linear optimization. The optimized design allows to conveniently redistribute mechanical power through different actuators, i.e. peak torque and velocity requirements for each actuator can be modulated, thus promoting a lighter design. A detailed analysis of the resulting mechanism workspace is carried out, including the evaluation of kinematic singularities, in order to verify the adequateness of the design in real-world scenarios. The developed model and optimization results are validated through a numerical analysis and experiments in a mock-up system.

This paper reports on an autonomous mobile robot embodying a simple instantiation of theDouble Hybrid Control Architecture, with a living animal in the control loop for improving robot navigational capabilities. In particular, a Carassius auratus (common Goldfish) receives a natural visual feedback from the environment, while its motor reactions, acquired by a digital camera and adequately processed, are mapped into motor commands, which are fed as inputs to the robot controller. Both the hardware and the software required for robot control is presented, as well as preliminary experimental data demonstrating the viability of the proposed approach.

The need for novel, high performance actuators felt in several fields of robotics, such as assistive or rehabilitative robotics, is not fully satisfied by current actuation means. This fosters an intense research on novel energy transduction methods. In particular, propellant-based chemical actuators, able to directly convert chemical energy into mechanical energy, appear very promising, although their potential in robotics has not yet been deeply investigated. This work focuses on H2O2, used as propellant for actuators. This chemical was first used in robotics, with excellent results, by Goldfarb and collaborators, in 2003. H2O2dissociation is strongly exothermic, which generates important design issues when the actuated machine operates in close proximity to the human body. In this paper it is shown that: 1) is possible to operate the decomposition process at acceptable temperature, by means of basic solutions of hydrogen peroxide; 2) for basic pH solutions, tin becomes an effective catalyst for H2O2dissociation. A kinetic model of H2O2dissociation in basic solutions is provided, that is in good agreement with experimental data. We show how the model can be used to gather the necessary information for the dimensioning of H2O2-based actuators.

This work proposes a mechatronic solution to increase the back-drivability of a state-of-the art robot for wrist neurorehabilitation. The final goal is to reduce robot mechanical impedance in order to cope with intrinsic kinematic constraints, which are adopted by the human brain to solve redundancy during pointing tasks with the wrist. The handle of the robot has been provided with a load cell and a direct force control scheme has been implemented to minimize the interaction forces/torques between the user and the robot. To this aim gravity, inertia and friction of the more proximal DOF of the robot (relative to Pronation/Supination (PS) movements) have been estimated and compensated for. The proposed solution resulted in a 70% reduction of the end-point perceived inertia in PS DOF as well as in a decrease of torques exerted by the user during both 1-DOF and 3-DOFs tasks. The average reduction of interaction torques is around 81% and 78% respectively. This work constitutes an important starting point for the analysis of the effect that different levels of robot transparency could have on the human neural constraints adopted during redundant tasks, such as pointing movements with the wrist.

In the design of wearable robots, the possibility of dynamically regulating the mechanical output impedance is crucial to achieve an efficient and safe human-robot interaction and to produce useful emergent dynamical behaviors. In this paper we propose a Variable Impedance Differential Actuator (VIDA) for wearable robotics applications. The system comprises two actuators (one being an impedance-controlled rotary Series Elastic Actuator) connected through a Harmonic Drive in a differential configuration used to separately control output position and mechanical impedance. Design choices regarding the overall architecture and the single components are presented and discussed. The mechanical structure also comprises a custom-made torsion spring designed after a CAD/FEM optimization. An electromechanical model of the system has been developed and a control strategy, based on the equilibrium point approach, is simulated to validate the performances of the system against system requirements. The actuation architecture allows to implement a control strategy where an equilibrium position and impedance field are simultaneously and independently regulated. This is possible still adopting very simple control laws: two controls for position and impedance regulation of the two input shafts.

In this work a multimodal head-mounted device for the assessment of social orienting behavior in children between 12 and 24 months is presented. The device is specifically designed to be used in poorly structured and uncontrolled environments such as day-care centers. Accordingly, a calibration procedure is described which fully exploits the multimodal approach and which is particularly suitable for an ecological assessment.

Humans have the ability to learn novel motor tasks while manipulating the environment. Several models of motor learning have been proposed in the literature, but few of them address the problem of retention and interference of motor memory. The modular selection and identification for control (MOSAIC) model, originally proposed by Wolpert and Kawato, is one of the most relevant contributions; it suggests a possible strategy on how the human motor control system learns and adapts to novel environments. MOSAIC employs the concept of forward and inverse models. The same group later proposed the hidden Markov model (HMM) MOSAIC, which affords learning multiple tasks. The significant drawback of this second approach is that the HMM must be trained with a complete data set that includes all contexts. Since the number of contexts or modules is fixed from the onset, this approach does not afford incremental learning of new tasks. In this letter, we present an alternative architecture to overcome this problem, based on a nonparametric regression algorithm, named locally weighted projection regression (LWPR). This network structure develops according to the contexts allowing incremental training. Of notice, interaction force is used to disambiguate among different contexts. We demonstrate the capability of this alternative architecture with a simulated 2 degree-of-freedom representation of the human arm that learns to interact with three distinct objects, reproducing the same test paradigm of the HMM MOSAIC. After learning the dynamics of the three objects, the LWPR network successfully learns to compensate for a novel velocity-dependent force field. Equally important, it retains previously acquired knowledge on the interaction with the three objects. Thus, this architecture allows both incremental learning of new tasks and retention of previously acquired knowledge, a feature of human motor learning and memory.

Ideally, robots used for motor rehabilitation, in particular, during assessment, should minimally perturb the voluntary movements of a subject. In this paper, we show how a state-of-the-art back-drivable robot, i.e., a robot that can be moved by the user with a low perceived mechanical impedance, when used for assessment can still perturb the voluntary movements of a subject. In particular, we show that, despite its low mechanical impedance, a robot may still not comply with the intrinsic kinematic constraints, which are of neural origin and are adopted by the human brain to solve redundancy in motor tasks. Specifically, the redundant task under consideration is the 2-D pointing task, which is performed by a subject with the sole use of the wrist [3 degree of freedom (DOF) kinematics]. Wrist orientations during pointing tasks are assessed in two different scenarios. In the first experiment, a lightweight handheld device is used, which introduces no loading effect. In the second experiment, similar pointing tasks are performed with the subject interacting with a state-of-the-art robot for wrist rehabilitation. In the first case, intrinsic kinematic constraints arise as 2-D surfaces embedded in the 3-D space of wrist configuration. Such surfaces are typically subject-dependent and reveal personal motor strategies. In the second case, a strong influence of the robot is remarked. In particular, 2-D surfaces still arise but are similar for all subjects and are referable to a mechanical origin (excessive loading by the robot). The assessment approach described in this paper, including both the experimental apparatus and data-analysis method, can be used as a test for the degree of back-drivability of mechanisms and robots in relation to constraints of neural origin, thus allowing the design of robots that can actually cope with such constraints. The clinical potential impact is also discussed.

The 16 papers in this special section have been logically organized into four different groups: enabling technologies for rehabilitation robotics; two robotic systems used for assisted diagnosis of pathologies of interest in the rehabilitation domain; different robotic systems applied to assisted physical rehabilitation; and robotic systems used for assisted psychophysiological rehabilitation.

Applications of robotics and mechatronics to neurorehabilitation are getting more and more consensus in the clinical community thanks to early encouraging results. They enable an objective assessment of patient's motor recovery and the administration of rehabilitation treatments specific for each patient. In particular, isometric force/torque measurements in post-stroke patients were recently used in clinical trials for the functional assessment, with encouraging results. A challenging issue in the processing of such measurements is to detect the initiation of the voluntary contraction of the patient (i.e., onset time). The onset detection is crucial to obtain clinically relevant data. In previous works, different deterministic methods for onset detection were presented. Each of those methods is signal-structure dependant, causing drop of performance when applied to different kind of signals. In this paper, we introduce an innovative technique for the automatic selection of the best onset detection method. To this aim, we adopt a supervised pattern recognition approach that dynamically selects, from a pool of deterministic methods, the one that is best suited for each signal according to the signal structure. The method has been tested on annotated force and torque datasets, showing that such a method improves not only the performance achieved by the single deterministic techniques, but also those attained by a group of clinical experts.

In this work, the kinematics of the human wrist during pointing tasks is assessed and discussed, especially in relation to the use of wrist robots. First, the existence of intrinsic kinematics constraints (or Donders' law, similarly to oculomotor system) for the human wrist during pointing tasks is verified. To this end, a novel approach based on a hand-held device is presented which allow assessing wrist movements without any mechanical loading. Second, similar pointing tasks are assessed by means of a state-of-the-art wrist robot, typically used in robot-mediated therapy as well as assessment tool. By comparing experimental results relative to the pointing task performed by healthy subjects with the hand-held device and with the robot, a loading effect on the performance due to the mechanisms of the robot is remarked. A functional requirement for the next generation of rehabilitation robots is thus provided.

Tremor analysis in human or animal model plays a fundamental role for understanding the physiopathology of human disorders and to test new pharmacological treatments. Mechatronic systems for automatic and quantitative behavioural analysis are now of current use for neuroscientists to improve the results of their research. Most of these devices are portable and need to be used out of engineering labs by personnel with no technical expertise and without specific equipment. The calibration of the devices, that should be performed before each experimental session, is a typical issue to be faced. This paper deals with a new calibration method that is fast, simple and doesn't need other external measurement systems. It is based on the parallel use of an accelerometer and an optical sensor. The two signals are processed and compared to obtain the final calibration curve of the device.

This paper presents the design criteria of a robot manipulator for the rehabilitation of the upper limb. The machine is conceived to optimize the dynamic behavior in the interaction with the patient, basically in terms of low impedance when back-driven and low and isotropic inertial properties. Robot kinematics and dynamics are mathematically modeled and its dynamic properties are measured in the workspace in terms of inertia and acceleration capabilities. A method is used that is borrowed from the literature and is based on inertia and acceleration graphical representation through ellipsoids.

In this paper, we study sensor fusion for the attitude estimation of micro aerial vehicles (MAVs), in particular mechanical flying insects. First, following a geometric approach, a dynamic observer is proposed which estimates attitude based on kinematic data available from different and redundant bio-inspired sensors such as halteres, ocelli, gravitometers, magnetic compass and light polarization compass. In particular, the traditional structure of complementary filters, suitable for multiple sensor fusion, is specialized to the Lie group of rigid body rotations SO(3). Then, a numerical implementation of the filter is provided for the specific case of inertial/magnetic navigation, i.e. when gravitometers, magnetometer and gyroscopes are available. Finally, the filter performance is experimentally tested via a 3 degrees-of-freedom robotic flapper and a custom-made set of inertial/magnetic sensors. Experimental results show good agreement, upon proper tuning of the filter, between the actual kinematics of the robotic flapper and the kinematics reconstructed from the inertial/magnetic sensors via the proposed filter.

There are recent evidence which show how brain development is strictly linked to the action. Movements shape and are, in turn, shaped by cortical and sub-cortical areas. In particular spontaneous movements of newborn infants matter for developing the capability of generating voluntary skill movements. Therefore studying spontaneous infants' movements can be useful to understand the main developmental milestones achieved by humans from birth onward. This work focuses on the design and development of a mechatronic wearable device for ecological movement analysis called WAMS (Wrist and Ankle Movement Sensor). The design and assembling of the device is presented, as well as the communication protocol and the synchronization with other marker-based optical movement analysis systems.

This paper presents the design of a novel sensor for slip detection. It consists of an easily fabricated miniaturized thermal probe that senses the additional convective heat transfer associated with the occurrence of mechanical slip. The fabrication procedures and the operating principle for the device are described in detail. A simple experimental setup was used to test the effectiveness of the proposed device. Tests were performed with varying velocities on four materials of differing thermal properties and surface roughnesses. The results show that slip can be effectively detected by the proposed sensor with a response times which can be as low as 6.3 ms. The performance of the device can be further improved when used in conjunction with a separate pressure sensor and by using more accurate methods of electrical resistance measurement.

Neuro-developmental engineering is a new interdisciplinary research area at the intersection of developmental neuroscience and bioengineering. Applications can be found in early detection of neuro-developmental disorders via a new generation of mechatronic toys for assessing the regular development of perceptual and motor skills in infants, in particular coordination of mobile and multiple frames of reference during manipulation. This paper focuses on the design of a novel mechatronic toy, shaped as a 5 cm (diameter) ball, i.e. small enough to be grasped with a single hand by a 1 year old child. The sensorized ball is designed to embed a kinematics sensing unit, able to sense both the orientation in 3D space and linear accelerations, as well as a force sensing unit, to detect grasping patterns during manipulation. Dimensioning of batteries able to operate for 1 hour during experimental sessions as well as a wireless communication unit are also included in the design.

Great amounts of raw data acquired with the use of an innovative mechatronic platform during an extensive clinical trial in a neuro-rehabilitation setting needs an analysis and interpretation. The platform records data from eight 6 DOF force-torque sensors during an isometric functional assessment of post-stroke patients. The identification of preprocessing parameters and onset detection methods, developed thanks to the close collaboration between biomedical engineers and clinicians, is presented in the paper. The present work presents also the implementation and testing of the software for the data pre-processing.

This paper presents a self-organizing neural network model for visuo-motor coordination of a redundant humanoid robot arm in reaching tasks. The proposed approach is based on a biologically-inspired model which replicates some characteristics of human control: learning occurs through an action-perception cycle and does not requires explicit knowledge of the geometry of the manipulator. The transformation learned is a mapping from spatial movement direction to joint rotation. During learning, the system creates relations between the motor data associated to endogenous movements performed by the robotic arm and the sensory consequences of such motor actions, i.e. the final position and orientation of the end effector. The learnt relations are stored in the neural map structure and are then used, after learning, for generating motor commands aimed at reaching a given point in 3D space. The work is an extension of (E. Guglielmelli, et al.) including the end-effector orientation control. Experimental trials confirmed the system capability to control the end effector position and orientation and also to manage the redundancy of the robotic manipulator in reaching the 3D target point even with additional constraints, such as one or more clamped joints without additional learning phases

In this work the problem of orientation tracking based on inertial/magnetic sensors is restated in geometric terms, in particular an intrinsic observer, i.e. an observer whose performance does not depend on a specific choice of coordinates, is derived on the Lie group of rigid body rotations SO(3). Measurements of the gravitational and geomagnetic fields are used to estimate orientation errors. A coordinate-free control law is defined on the Lie algebra and fed back in terms of angular velocity that steers the observer towards the correct attitude. A proof of stability for the proposed estimator is provided which relies on the natural (bi-invariant) metric of SO(3). The observer results stable for almost the whole configuration space. Presence of unstable equilibria as a limitation for global stability is also discussed. Based on the proposed intrinsic control law, a filter is designed which implements the observer. Simulations are presented that test the numerical implementation of the proposed observer

This paper presents a new biphasic electrofluidic rotary actuator relying on electrochemical mechanisms. Gas (H2and O2) is generated at two platinum (Pt) electrodes, immersed in an electrolytic solution, when a low voltage, V, is applied to them. The generated gas pressurizes a closed elastomeric chamber, that deforms under the action of the applied pressure. The chamber is shaped so that no structural failure occurs and the desired output motion is produced, without any need for additional mechanical means such as motion converters. Simple thermodynamic considerations allow estimating the electromechanical coupling factors for the most common external loads.

This paper presents the application of a neural approach in the control of a 7-DOF robotic head. The inverse kinematics problem is addressed, for the control of the gaze fixation point of two cameras mounted on the robotic head. The proposed approach is based on a biologically-inspired model, which replicates the human brain capability of creating associations between motor and sensory data, by learning. The model is implemented here by self organizing neural maps. During learning, the system creates relations between the motor data associated to endogenous movements performed by the robotic head and the sensory consequences of such motor actions, i.e. the final position of the gaze fixation point. The learnt relations are stored in the neural map structure and are then used, after learning, for generating motor commands aimed at reaching a given fixation point. The approach proposed here allows to solve the inverse kinematics and joint redundancy problems for the ARTS robotic head, with good accuracy and robustness. Experimental trials confirmed the system capability to control the gaze direction and fixation point and also to manage the redundancy of the robotic head in reaching the target fixation point even with additional constraints, such as a clamped joint or two symmetric joint angles (e.g. eye joints).

In recent years, advances and improvements in engineering and robotics have been strengthening interactions between biological science and robotics in the goal of mimicking the complexity of biological systems. In this paper, motor control paradigms inspired by human mechanisms of sensory-motor coordination are applied to a biologically-inspired, purpose-designed robotic platform. The goal was to define and implement a multi-network architecture and to demonstrate that progressive learning of object grasping and manipulation can greatly increase performance of a robotic system in terms of adaptability, flexibility, growing competences and generalization, while preserving the robustness of traditional control. The paper presents the neural approach to sensory-motor coordination and shows preliminary results of the integration with the robotic system by means of simulation tests and experimental trials.

Movement and behavior analysis is a key research area in the domain of biomedical engineering and in many other medical research domains aiming at the understanding of physiological motor and cognitive basic mechanisms. The systematic application of robotic and mechatronic technologies to realize new tools and measurement methods for quantitatively assessing motor and cognitive functions in humans as well as in animal models is gaining an increasing popularity. This work represents a first step towards the development of a sensorised environment for behavioral phenotyping of animal models. In particular, this paper focuses on tremor analysis in reeler mice, an emerging potential animal model for anatomical and behavioral traits observed in autism. Ground reaction force (GRF) sensing is indeed the most direct means of measuring tremor. Although force platforms have extensively been used for large size animals, only few attempts have been made to measure GRF at a single paw for animals as small as mice or rats. Under the hypothesis that in-plane GRF components are directly connected to tremor, a small size, low-cost, 2-axis force sensor for measuring the in-plane components of GRF was designed and developed. Special care was paid to design a structure that would allow self-aligned assembly, for repeatability, and modularity for combining multiple platforms for a sensorised floor. Preliminarily testing was performed with both reeler and wildtype mice. Fourier analysis was deployed to extract information due to tremor, validating the hypothesis of a direct connection between tremor and in-plane GRFs.

Studies of human motor control suggest that humans develop internal models of the arm during the execution of voluntary movements. In particular, the internal model consists of the inverse dynamic model of the musculoskeletal system and intervenes in the feedforward loop of the motor control system to improve reactivity and stability in rapid movements. In this paper, an interaction control scheme inspired by biological motor control is resumed, i.e. the coactivation-based compliance control in the joint space (Zollo, L, et al., 2003), and a feedforward module capable of online learning the manipulator inverse dynamics is presented. A novel recurrent learning paradigm is proposed which derives from an interesting functional equivalence between locally weighted regression networks and Takagi-Sugeno-Kang fuzzy systems. The proposed learning paradigm has been named recurrent locally weighted regression networks and strengthens the computational power of feedforward locally weighted regression networks. Simulation results are reported to validate the control scheme.

This paper describes an experimental work conducted in order to estimate the conditions of applicability of expected perception (EP) based on a scheme for robot sensorimotor coordination. The starting hypothesis is that predictions of incoming sensory data can improve sensorymotor coordination respect to pure feedback loops. This implies that the environment presents a level of predictability, as in realistic environments. An implementation of the EP-based scheme has been realized on a platform composed by the Dexter 8-d.o.f. robotic arm and a color camera, for executing a pushing task in a real-world environment. Its performance, where defined as a combination of the error in the trajectory following and the computational effort, has been compared with that of a feedback-based system executing the same task in the same environmental conditions. The results have been put in relation with the degree of environmental predictability, which was controlled in the experimental trials. The experimental results give support and useful insights for analyzing the applicability of the EP-based scheme.

In this paper a wearable biomechatronic system (named MEKA) for the analysis of knee movements in two degrees of freedom is presented. The system has been designed in order to be used not only in laboratory environment (as many other commercial systems) but also in real-life conditions (e.g. for experiments on sport biomechanics). The performance of the MEKA device and of two commercial systems (an electrogoniometer and a camera-based motion analyzer) has been compared. The results showed that the MEKA system is able to obtain similar or even better performance during movements. After this preliminary assessment phase, the MEKA system has been used to analyse the modifications of motor performance of elderly and young people during gait using a "dual-task" approach. The results show that elderly people seems to be more affected by the increase cognitive efforts. In conclusion, the MEKA system seems to be able to provide interesting information for experiments on biomechanics and motor control.

This paper presents the design and development of a 3-axial artificial vestibular system to be integrated on robotic heads, in order to provide a sense of head position and motion. In accordance with the role of the vestibular system in humans, this artificial vestibular system is specifically devoted to regulate and stabilize gaze during head motion, by means of the vestibulo-ocular reflex (VOR) mechanism. Future applications explore the possibility of using the artificial vestibular system on human heads, as an indirect interface between humans and robots, for 'tele-control'.

The application of robots in contact with humans, in services or assistance, implies the realization of effective and acceptable human-robot interaction. In humans, vision plays a significant role in social interaction, like for example in the recognition of faces and expressions, as well as in gesture understanding. Visuo-motor coordination of eye and neck movements in humans presents high performances in terms of accuracy, speed, effectiveness. This is due to the mechanical, cinematic, and dynamic features of the head muscular-skeletal apparatus and to the peculiar processing of visual data, detected with a space variant resolution. The work presented in this paper aims at employing retina-like cameras in human-like robotic head, reproducing similar degrees of freedom, ranges of motion, speeds, and accelerations of human neck and eye movements, in order to improve visuo-motor coordination. The use of retina-like cameras allows a faster human-like processing of visual information, better suited for the control of head movements. On the other hand, retina-like cameras provide high resolution information only in a small area of the image and thus need to be dynamically focused on the points of interest. Experimental trials were focused on the capability of identifying and tracking human faces, as a first step of human-robot interaction. Preliminary experimental results show the feasibility of a smooth face tracking, by a closed control loop for the head movements, based on retina-like vision.

The aim of this paper was to analyse the feasibility of using a sensor-based glove as a tool for the assessment of hand function in persons with orthopedic disorders such as rhizoarthrosis. A glove embedding 20 Hall-effect sensors was used for this purpose. Its performance in terms of repeatability were preliminary investigated in order to define an effective strategy for the use of the glove in these applications. After this preliminary phase, focused clinical experiments were carried out in order to verify the existence of differences usable for performance assessment in the postures of the hand of able-bodied and subjects affected by rhizoarthrosis during grasping. The data were processed by using the principal component analysis (PCA) technique. The preliminary results of these experiments showed that the use of the first and second principal components can allow to discriminate between able-bodied and disabled persons and also between disabled persons before and after the surgical intervention. For this reason, this approach could represent an effective tool for the surgeon during the rehabilitation process in this particular situation. Further experiments will be carried out in the future in order to confirm these preliminary results. Moreover, a new calibration procedure of the sensor-based glove will be developed in order to improve the performance of the glove.

Neurophysiological studies show that humans possess the capability of generating appropriate motor behaviors to different uncertain environmental conditions by combining a forward action, produced by the internal forward dynamic model, and a feedback control, realising the transformation from sensory information to motor commands. To this regard, a control system based on the combination of a feedforward and a feedback control loop has been developed in order to provide a robot arm with human-like adaptation capabilities. The work analyses the role of biological coactivation in the mechanism of adjustable visco-elastic arm properties and proposes a function for the evaluation of the robot arm coactivation based on the measure of the position error and the interaction force. The coactivation function is used to update the proportional and derivative parameters of the feedback controller and, consequently, the arm visco-elasticity in unpredictable environmental conditions. Finally, experimental results on the evolution of the coactivation in the adaptation and de-adaptation phases are provided in the last section of the paper.

The paper proposes an anticipation mechanism to improve the perception-action loop of robots interacting with real-world environments. According to recent neuroscientific findings, sensory anticipation can increase the effectiveness of perception-action loops and reduce the delays in obtaining the sensory information, especially in case of complex sensory modalities like vision, that affect pure feed-back structures. In the proposed scheme, perception crucially involves comparison processes between incoming stimuli and expected perceptions (EPs), built from previous perceptions, current motor commands, and internal models of the robot and the environment. Background knowledge plays here a helpful role, as it reduces the computational burden of perception and motor coordination tasks in partially structured environments. In the work presented here, an EP mechanism has been applied in the visuo-motor coordination of an anthropomorphic 8 d.o.f. robotic manipulator equipped with a vision system, in order to evaluate the conditions of applicability of the proposed strategy, and to validate the viability and effectiveness of the initial hypothesis.

The development of humanoid robots and their application as personal robots introduce problems related to the interaction of robots with humans and with environments specifically designed for human beings. In tasks where human-robot cooperation is required, compliance of the robot arm can provide a helpful solution, not only to ensure safety, but especially to increase the robot functionality and usability. The concept of functional compliance is illustrated and an experimental validation is reported, where the functionality of an anthropomorphic robot arm is comparatively assessed with and without compliant control. The paper describes the implementation of a compliant control scheme on an anthropomorphic robotic manipulator, illustrates in detail the methodology adopted for the experimental comparative validation and reports the results obtained in the execution of a sample task by a set of potential users, showing the increased performance in the case of compliant control. Indications are also provided on the improvement of acceptability, which is also affected by the enhanced performance.

The aim of the work is the integration of an anthropomorphic robotic platform, starting from a neurophysiological model of grasping, in order to provide tools for an experimental validation of the model and also to provide new anthropomorphic solutions for robot grasping. The resulting robotic system is composed of an anthropomorphic arm/hand system and visual and tactile sensors. Grasp planning, control and learning have been achieved by a neural approach, inspired by a model of the inter-connections among the brain areas involved in grasping, as formulated by neurophysiologists. After a description of objectives and specifications for the robotic platform, the system is illustrated and experimental results are reported. Finally, the results are discussed as a starting point for current activities, involving the development of novel human-like robotic components and the implementation of more sophisticated learning schemes.

A research work on the interaction control of a cable-actuated robot arm, the Dexter arm, is presented in this paper. Firstly, general considerations on the cable-actuated structures and their application potential are provided and then the Dexter structure peculiarities are accurately analyzed in order to develop proper control solutions. Starting from the analysis of the limitations of the compliance control schemes in Cartesian space and in joint space, previously implemented and experimentally validated on the Dexter arm, a novel control strategy, named impedance-compliance controller, is developed. The proposed control strategy tries to combine the benefits of a compliance control scheme in Cartesian space with the benefits of an impedance control scheme in the operational space by compensating the dynamics of the sole proximal joints. The impedance-compliance controller is capable to achieve accurate smooth motions while guaranteeing functional control of the whole structure, even though a greater computational complexity is required The last section of the paper, dedicated to the experimental results, points out the differences with the previously experimented control solutions anti provides some proofs of the increased Dexter functionality.

The field of humanoids robotics is widely recognized as the current challenge for robotics research. Developing humanoids poses fascinating problems in the realization of manipulation capability, which is still one of most complex problem in robotics. The paper, starting from an overview of current activities in the development of humanoid robots, with special focus on manipulation, presents the authors' approach to the design and development of anthropomorphic sensorized hands and of anthropomorphic control and sensory-motor coordination schemes. Current achievements at the Scuola Superiore Sant'Anna and Centro INAIL RTR (Research Centre on Rehabilitation Bioengineering) in the development of a robotic human prosthesis are described, together with preliminary experimental results, as well as in the implementation of biologically-inspired schemes for control and sensory-motor co-ordination, derived from models of well-identified human brain areas.

In this paper we describe our approach to the design of proximity sensor arrays. Each sensing element of the proximity sensor array is a composite sensor, i.e. a sensor which is composed of an in-air ultrasonic rangefinder and an infrared detector. This sensor arrangement is capable, in principle, to achieve a perception of the explored objects that is not necessarily limited to their geometrical properties (size, shape and location relative to the sensor); possibly, the perception can be extended to other relevant features. In this paper, we show that, in principle, a sort of perception of the surface reflectance (the color) is achievable. The concept of the proposed sensor array spans a wide range of potential applications in flexible industrial automation, service robotics and autonomous mobility. The system described, in particular, is intended for providing an advanced wheelchair with the navigational capabilities required for improving the driving skills of disabled users.

This paper reports the current state-of-the-art in medical robotics. Three general areas of advanced robotics are identified: macro robotics, micro robotics and bio-robotics. Macro robotics include the development of robots, wheelchairs, manipulators for rehabilitation as well as new more powerful tools and techniques for surgery. Micro robotics could contribute to the field of minimally invasive surgery as well as to the development of a new generation of miniaturised mechatronic tools for conventional surgery. Bio-robotics deals with the problems of modelling and simulating biological systems in order to provide a better understanding of human physiology. According to this classification, a review on the most important past and ongoing research projects in the field is reported. Some commercial products already appeared on the marker are also mentioned, and a brief analysis of the economical potentialities of robotics in medicine is presented.>

This paper reports part of the work related to the design and development of the URMAD system, a mobile robotic unit purposely devised to assist the severely disabled in a household environment. A supervisory system based on distributed decision making and functional distribution has been designed and implemented on a VME/OS9 architecture. This system is capable of executing typical domestic tasks expressed in a synthetic command language extracted from everyday vocabulary. The user's requests can be easily specified by using a Windows-like graphical user interface and are translated into the command language by an interpreter running on a personal computer. In the paper, a general overview of the URMAD system is provided. A detailed description of the proposed modular architecture for the supervisory system, of its various modules and of the command language, is presented. Finally, the hardware and software implementation of the first prototype of the system and some preliminary experimental tests and simulations are reported.>

The problem of developing a sensing system for robots moving in a partially or completely unknown environment is investigated. A model of a generalized sensitive skin based on different types of sensors to be located on the whole surface of a robot is illustrated. A logic and hardware structure of the low-level layer of the skin is proposed to guarantee modularity and consistency of sensory data. A configuration including ultrasonic and infrared proximity sensors was chosen for the implementation of a prototype of the skin. A possible physical layout for obtaining a conformable skin to be used on any kind of robot surface is also presented. Local and general rules for low-level sensory data fusion are defined, and possible improvements of real-time obstacle avoidance path planning strategies are discussed by introducing sensor-based instinctive behaviors. A simulation of a modified potential field-based real-time planner for a mobile robot operating in a partially unknown environments is discussed to demonstrate the feasibility of this approach.