Maintaining motivation and sustained engagement in pediatric neurorehabilitation remains a significant challenge, particularly for children with neuromotor impairments. Traditional therapy methods often lack personalized adaptability, which can limit adherence and effectiveness. The proposed framework addresses this gap by integrating multimodal sensor data, including EEG, posture, and gaze, to continuously monitor emotional, cognitive, and behavioral engagement during therapy sessions. This real-time assessment enables dynamic adaptation of game-based exercises with the aim of optimizing motivation, reducing disengagement, and promoting functional recovery. This concept was implemented in a clinical setting with children diagnosed with coordination disorders and neuropsychomotor delays. Preliminary results with four participants indicate that by tracking engagement levels and supporting session personalization, it is possible to stimulate the child’s motivation across multiple sessions. These findings suggest that incorporating adaptive, engagement-driven frameworks can provide a useful tool to improve rehabilitation efficacy, offering a way toward more personalized and responsive therapeutic strategies in pediatric neurorehabilitation.

People's successful coexistence with robots strictly depends on people's acceptance of robots' presence in their daily activities. This is particularly relevant when the robot's actions may interfere with or intrude on people's activities, creating discomfort and possible rejection. We believe that people's acceptance of a robot may vary depending on the activities they are involved in. In this study, we investigate the impact of a robot's actions on people's engagement in an activity while the robot has the task of monitoring them. We observed the behaviours of 18 older adults with respect to the robot while they were carrying out tasks that require different cognitive workloads (e.g., working at the PC, talking on the phone). We used subjective and objective metrics, such as social cues, to evaluate people's engagement in the robot and their disengagement in their own tasks. We observed that people were distracted by the robot's behaviours based on the cognitive loads required by their activity. Our results show that variation in people's engagement in the robot and the task is affected by their perception of the usefulness of and trust in the robot, and by individuals' personality traits and acceptance of the robot. People with higher trust in the robot, and a higher degree of conscientiousness and emotional stability, tend to continue with their task, paying less attention to the robot. We observed, in contrast, that a robot perceived as a social entity caught more easily their attention when people have a higher extroverted personality. Our findings also showed that variations in the affective and emotional demeanour of the participants are a predictor of their distraction to an external observer.

Assistive robots can be valuable conversational partners for cooperative tasks, such as storytelling, fostering creativity and social bonding. Through the use of foundational models, such as LLMs, robots can more effectively and naturally generate story narrations that are enjoyed by humans. In such a scenario, however, it is fundamental to consider the users’ feedback and reactions to adapt the story and the interaction in a way that actively sustains their interest. In this work, we propose an LLM-assisted storytelling generation method that employs different robot’s communication modalities to stimulate the user’s behavioral, affective, and cognitive engagement during the interaction and affect the narration of the story. Moreover, we investigated the introduction of an adaptive interaction policy to choose the most suitable actions based on the user’s observed engagement. We conducted a user study with 36 participants to assess our proposed approach, and demonstrated that it manages to effectively assist participants in an engaging way, with the robot being perceived as friendly and trustworthy. Moreover, the policy adaptation results in a perception of the robot with a higher arousal while a more interactive approach led to a better perceived social intelligence.

Food recommender systems have become valuable tools across various domains, including health-oriented applications that provide personalised dietary advice. Recent studies have shown the potential of integrating recommender systems with assistive robots to promote healthy eating habits, especially among older adults. While transformers and Large Language Models showed advanced reasoning capability for effective recommendation systems, they might have limited knowledge and understanding of the users’ personal preference and requirements. This lack of information can negatively affect their effectiveness and user’s satisfaction. We present a novel transformer-assisted, multi-interface recommendation system for generating food recommendations based on user profiles using a custom dataset including dietary and nutritional information. We conducted a user study with 40 participants for evaluating whether a robot is able to persuade users’ in accepting its food recommendation. Our study found that participants responded positively to the interactions with the robot, showing high satisfaction and trust in the recommendations.

Human-robot interaction in cooperative and assistive scenarios requires robotic systems to assess the task state and coherently choose their next move.Moreover, it is also fundamental to correctly recognize how the user's stress and emotional response are changing to offer support appropriately.The robot should be able to adapt to different user reactions, considering the situational context, and displaying empathetic behaviors aiming to support and encourage the users.In this work, we aim to assess the impact of empathetic supporting behaviors on the perception of the robot and the users' performance during a collaborative task, as opposed to assistive strategies focusing only on the task's performance.With this objective in mind, we propose a robotic architecture to assist a user in playing a memory game in real-time using a Furhat robot.We conducted a user study where 60 participants played with the robot to evaluate the effects of the two types of Theory of Mind on the assistive task and their perception of the robot.To this extent, the participants interacted with a robot endowed with either Cognitive or Affective Theory of Mind to respectively allow the robot to understand intentions and beliefs, and to show empathetic behaviors to improve the collaboration.The two conditions resulted in achieving the same results in terms of task performance, but the participants rated the emotionally engaged robot higher in perceived social intelligence.

The number of contexts in which a word is encountered, or contextual diversity, has been shown to be a relevant predictor of word-naming and lexical decision times. In this work we present an end-to-end scenario in which we collect data with a humanoid robot in three different contextual diversity levels, use the data to train a cognitive architecture with the objective of mirroring the same phenomenon observed in the literature, and ultimately we test the model by collecting test data with the robot and matching them with the learned word-object mappings. Results show that the approach manages to capture and describe successfully a computational representation of the impact of contextual diversity on word-object mapping, showing how with greater contextual diversity the mapping is more precise compared to the cases with lower diversity.

Abstract We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child’s current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child’s performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments.

In this paper we advocate the use of Inductive Logic Programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ILP system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for identifying unwanted biases when the original dataset is not available. Finally, we highlight directions for further work and discuss relevant application areas.

In this work, we introduce a set of robot's behavior aimed at being used for monitoring and interaction with elderly people affected by Alzheimer disease. Robot's behaviors for a low cost robotic device rely on the use of microservices running on a local server. A microservice is an independent, self-contained, self-scope, and self-responsibility component of the robotic system proposed for decoupling the implemented functions needed to obtain the proper robot behaviors. The developed robotic behaviors include navigation, interaction, and monitoring capabilities. The requests and the signals of the patients are handled and managed relying on event-based communications between the system components. The use of design patterns like this one increases the overall reliability of a service composition. The system is currently operating in a private house with an elderly couple.

Companion robots used in the field of elderly assistive care can be of great value in monitoring their everyday activities and well-being. However, in order to be accepted by the user, their behavior, while monitoring them, should not provide discomfort: robots must take into account the activity the user is performing and not be a distraction for them. In this paper, we propose a Reinforcement Learning approach to adaptively decide a monitoring distance and an approaching direction starting from an estimation of the current activity obtained by the use of a wearable device. Our goal is to improve user activity recognition performance without making the robot's presence uncomfortable for the monitored person. Results show that the proposed approach is promising for real scenario deployment, succeeding in accomplishing the task in more than 80%of episodes run.

Socially Assistive Robots used for elderly care are required to determine the location of a person and to approach him/her in order to provide assistance. Human tracking systems are applied to detect and track people that are already in the proximity of the robot, while its limited field of view makes the user easily lost. Moreover, navigation algorithms typically need the availability of reliable sensors on the robot and the possibility of marking possible user locations. On the contrary, in this work, we investigate the opportunity to use a reactive control mechanism for detecting and approaching people. Our approach is tested on two commercial mobile robots that present a different sensors configuration and by using off-the-shelf algorithms for people localization and tracking. Results show the feasibility of the approach with respect to the considered domain that does not require precise positioning, but hopes for a real application of such low-cost robot into the wild. Features of the considered robots and their impact on performance are also discussed.

Social Assistive Robots are mainly designed for tasks requiring the interaction with the user. However, they could also be involved in other non-interactive tasks which execution may potentially distract the user from his/her current activity. In this direction, we aim at evaluating the disengagement levels caused by a robot's movements in the human's surroundings. In particular, we consider a robotic system approaching an elder person to monitor his/her behavior, while he/she is occupied in carrying out a specific daily activity. We conducted a classic video analysis of human behaviors in order to identify relevant non-verbal disengagement signals such as the human gaze and pose variation when he/she is approached by the robot. Results obtained from ambient cameras are compared with the ones from the robot camera showing a moderate correlation between them. Additionally, the role of other contextual factors, such as the activity posture, approaching distance, and cognitive load are discussed in the direction of an automatic evaluation of the user's distraction to be used by the robot to plan its motion.