Educational robotics is increasingly merging into school curricula. Understanding the subjective perceptions of robots, particularly among children, requires nuanced approaches. In this paper, we employed a qualitative coding analysis method to explore how children of different ages conceptualise robots through drawings, and how prior experiences with robotics influence their perceptions. Our findings reveal that the perception of robotics is influenced by cognitive development stages, which is in turn affected by age, and by educational robotics. The latter plays a significant role in shaping perceptions of robots, fostering positive attitudes and aiding cognitive development, particularly in first-grade students. Our insights can inform both teachers to better tailor their educational robotics activities for different age groups and robot designers themselves. For instance, our findings highlight the importance of emotional expression and the preference for humanoid robots among primary school children.

Social robots are expected to become more and more used in the education field. However, in the interaction between children and social robots, how robots are perceived in social contexts is still under investigation. In this exploratory study, we aimed to investigate how children’s expectations and demographical characteristics (N= 53, 9-14 years old) influence their perception of robot NAO during an education training program in schools. MANCOVA analysis conducted over questionnaire data indicates a positive correlation between the acceptance of the robot and the enjoyment of interacting with it. We found evidence that the more students accepted the robot, the more they perceived the group environment positively. Through a Correspondence Analysis, we investigate which are the preferred features of a robot according to the age of participants. The study suggests that a better opinion of robotics is a factor that can improve the learning environment in this specific context. Our exploratory study encourages conducting studies in-the-wild using self-reported measures to understand the implication of Child-Robot Interaction better.

This article explores the acceptance of a humanoid robot designed to engage in conversations with clinicians and individuals with spinal cord injuries. Building upon prior research, we introduce the concept of “diversity-aware” robots, which possess the capability to interact with people while adapting to their culture, age, gender, preferences, and physical and mental conditions. These robots are connected to a cloud system specifically designed to consider these factors, enabling them to adapt to the context and individuals they interact with. Our experiments involved the NAO robot interacting with both clinicians and individuals with spinal cord injuries in a hospital environment. Subsequent to the interaction, participants completed a questionnaire and underwent an interview. The collected data were analyzed to assess the system’s acceptability and its persistence beyond the initial novelty effect. Furthermore, we investigated whether clinicians exhibited a lower predisposition towards the system and expressed greater concerns than end-users about using the robot, which could potentially hinder the adoption of the system.

Human motion analysis is an essential task in several domains and, depending on the application field, it requires different level of accuracy. In the motor control field it is commonly performed with motion capture systems and infrared markers that guarantee a high accuracy. However, these systems are expensive, cumbersome, and may induce bias. An alternative to marker-based technologies are image-based marker-less systems, that are cheaper and do not affect the naturalness of the motion. Although their accuracy level seems to limit their use in motor control field, a thorough quantitative comparison with marker-based techniques does not appear to be available yet. We compare the estimates of a 3D image-based marker-less pipeline we propose, with a standard marker-based system; the analysis is carried out on a multi-sensor dataset acquired to study the motion of violin players. The results we obtain on the precision level are suggesting that marker-less systems may successfully track performances in real-world settings.

In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body-machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users' task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process.

Sarcopenia is the loss of skeletal muscle tone, mass and strength associated with aging and lack of exercise. Its incidence is increasing, due to the growth in the number and proportion of older persons in world's population. To prevent the onset of Sarcopenia and to contrast its effects, it is important to perform on a regular basis physical exercises involving the upper and lower limbs. One of the main problems is to motivate elderly people to start a training routine, even better if in groups. This determines the need of innovative, stimulating solutions, targeting groups of people and easily usable. The primary objective of this study was to develop and test a new method for answering this need. We designed a platform where the humanoid robot Pepper guided a group of subjects to perform a set of physical exercises specifically designed to contrast Sarcopenia. The robot illustrated, demonstrated, and then performed the exercises simultaneously with the subjects' group. Moreover, by using an additional external camera Pepper controlled in real time the execution of the exercises, encouraging Participants who slow down or did not complete all the movements. The processing offline of the recorded data allowed estimating individual subjects performance. The platform has been tested with 8 volunteers divided into two groups. The preliminary results were encouraging: participants demonstrated a high degree of satisfaction for the robot-guided training. Moreover, participants moved with almost synchronously, indicating that all of them followed the robot, maintaining engagement and respecting the correct timing of the exercises.

A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.

This paper presents the design, and performance of a high fidelity three degree-of-freedom wrist exoskeleton robot, for neuroscience study, training and rehabilitation. The IIT-Wrist is intended to provide kinesthetic feedback during the training of motor skills or rehabilitation of reaching movements. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuriesIn the present paper the IIT-Wrist haptic robot is described in terms of kinematics and haptics features to meet specific requirements for a safety human.machine interaction. In relation with a feasibility study in the field of robot therapy a preliminary training of stroke patient was perfrormed. The task consisted in tracking a target using one degree of freedom at time: Flexion/Extension, adduction/abduction, pronation/supination separately. The target motion is harmonic and tracking is aided by a suitable force field. The preliminary study with three patients shows the stability and the efficacy of the control scheme.