A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

The COVID-19 pandemic will have a profound and long-lasting impact on the entire scientific endeavor. Scientists already are adapting research programs to adapt to changes in what is prioritized—and what is possible; educators are changing the way that the next generation of researchers are trained, and flagship conferences in many fields are being cancelled, postponed, and fundamentally transformed. These broad-reaching changes are particularly impactful to human-oriented domains such as human-robot interaction (HRI). Because in-person human-subject experiments can take a year or more to conduct, the research we will see published in the field in the immediate future may appear to be “business as usual,” with accounts of laboratory studies with large numbers of in-person participants. The research currently being performed, however, is of course a different story entirely. Studies that were under way when the current crisis began will be truncated, resulting either in work that cannot be published or in work whose true impact is difficult to accurately assess. Yet HRI research performed in the coming years will be changed in fundamentally different ways; the inability to perform—or expect future performance of—in-person human subjects research, especially research involving tactile or multiparty interaction, will change both the dominant methodological techniques employed by HRI researchers and the very research questions that the field chooses to—and is able to—address. These challenges demand that HRI researchers identify precisely how the field can maintain research quality and impact while the ability to conduct human-subject studies is severely impaired for an undetermined amount of time. A natural inclination may be simply to wait the crisis out in the hope of a speedy return to normalcy; however, in this article, we argue that the community can also take this opportunity to reevaluate and refocus how research in this field is conducted and how students are mentored in ways that will yield benefits for years to come after the current crisis has ended.

An instrument for remote control of robot by wearable Brain Computer Interface is proposed for rehabilitating children with attention deficit/hyperactivity disorder (ADHD). Augmented Reality (AR) glasses generate flickering stimuli and a single-channel electroencephalographic Brain Computer Interface detect the elicited Steady State Visual Evoked Potentials (SSVEP). This allows to benefit from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device’s wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on 10 healthy adult subjects highlighted an average accuracy higher than 83%, with Information Transfer Rate (ITR) up to 39 bits per minute. Preliminary further tests on 4 ADHD patients between 6 and 8 years old provided highly positive feedback on device acceptance and attentional performance.

Socially assistive robotics (SAR) is getting a lot of attention for its potential in assisting elderly users. However, for robotic assistive applications to be effective, they need to satisfy the particular needs of each user and be well perceived. For this purpose, a personalization based on user’s characteristics such as personality and cognitive profile, and their dynamic changes is a crucial factor. Moreover, most of the existing solutions rely on the availability of specific technological infrastructures, generally requiring high economic investment, and that cannot be easily placed in different environments. Personalization and adaptation of assistive robotics applications to different user’s characteristics and needs, and even to different technological environments, are still not fully addressed in real environments. In the present work, the results of the UPA4SAR project are presented. The project aimed at providing a social robotic system to deliver assistive tasks for home care of patients with mild cognitive impairment in a personalized and adaptive way. We introduce the general architecture of the system and the developed robotic behaviors. Personalization and dynamic adaptation of assistive tasks are realized using a service-oriented approach by taking into account both user’s characteristics and environmental dynamic conditions. Field experimentation of the project was carried out with 7 patients, using the robotic system autonomously running in their homes for a total of 118 days. Results showed a reliable functioning of the proposed robotic system, a generally positive reaction, and a good acceptability rate from patients.