Robust autonomous navigation is one of the most important aspects in the acceptance of social robots by elderly users. Traditional model-based navigation techniques provide a stable theoretical and practical foundation for autonomous operation in domestic environments, but fall short in achieving human-like, acceptable behaviour while still being able to robustly navigate cluttered environments. In this work, we propose ClusterNav, a novel learning-based technique for navigation. Our technique consists of teaching the robot how it should move in the environment in a human-like manner, capturing key features of this demonstration in a geometric representation of the environment. This representation is then used to generate new trajectories for execution, allowing the robot move in an acceptable manner. We have tested our technique in a real environment in an elderly care facility, comparing it with the traditional model-based approach. Tests involved both expert and non-expert users teleoperating the robot. Results show that ClusterNav is capable of navigating the environment, achieving better similarity with the reference trajectories and higher execution speed when compared to the model-based approach.

The purpose of this paper was to understand how an agent's performance is affected when interaction workflows are incorporated in its information model and decision-making process. Our expectation was that this incorporation could reduce errors and faults on agent's operation, improving its interaction performance. We based this expectation on the existing challenges in designing and implementing artificial social agents, where an approach based on predefined user scenarios and action scripts is insufficient to account for uncertainty in perception or unclear expectations from the user. Therefore, we developed a framework that captures the expected behavior of the agent into descriptive scenarios and then translated these into the agent's information model and used the resulting representation in probabilistic planning and decision making to control interaction. Our results indicated an improvement in terms of specificity while maintaining precision and recall, suggesting that the hypothesis being proposed in our approach is plausible. We believe the presented framework will contribute to the field of cognitive robotics, e.g., by improving the usability of artificial social companions, thus overcoming the limitations imposed by approaches that use predefined static models for an agent's behavior resulting in non-natural interaction.

In this work we propose a Bayesian User Model which is able capture a unified representation of cultural attributes from heterogeneous information in the context of Human-Robot Interaction. Despite the latest advances in robotic technologies, virtually no robots are able to cope with the specificities of the “modus vivendi” of different cultures. We start by proposing Bayesian classifiers to capture unitary attributes of different users, clustering them in a n-dimensional semantic attribute space, aggregating groups of persons that share similar attributes. Results show a highly accurate classification framework, both capable of detecting specific subtleties in user's properties, and generalizing them into representative profiles. We then discuss its application towards adapting the actions of a robot and its potential impact on culture-awareness, demonstrating how the proposed framework can enable culture-awareness, exploring this new frontier in social robotics.

In this work we present a Bayesian User Model for inferring the characteristics and inter-user patterns of a population users. The model can receive evidence gathered by various interactive devices, such as social robots or wearable devices. The system is modular, with each module being responsible for gathering information and observations from persons present in the system's operation scenario. This information enables each module to determine a single characteristic of the person. New observations and measurements received by the system are fused with previous knowledge by a sub-process based on an information theory technique. This allows the system to be implemented in diverse heterogeneous distributed system topologies, extending beyond robotics. We have conducted experiments involving a team of social robots and simulated user population. Our experiments have shown that the system is able to learn and classify the persons' characteristics, and to find relevant user groups via clustering. This system can potentially be used to gather information on a large set of persons, as well as to be an information source for user-adaptive applications in areas such as Robotics, Ambient Assisted Living (AAL) and Internet of Things.

The ability of robots to dialogue with humans appears as one critical Human-Machine Interaction feature when it comes to transferring robots into society. This ability gains additional importance when it comes to elderly people, since they find it more comfortable and natural to interact using voice, due to possible natural physical impairments that hinder the usage of some of the interaction modalities (e.g. touch screens). Challenges like recognition accuracy, distant speech, the idiosyncrasies of elderly voices (fading, muffled pronunciation, etc.), the effects of surrounding environment noise or the expressiveness of the robot when speaking, become highly relevant in the acceptance and usability of service robots by the ageing population. In this paper, we present the results, challenges and solutions developed during a nine-month iterative evaluation process that took place within the GrowMeUp project, with focus on speech recognition and synthesis. The paper concludes with an identification of open scientific and technological problems, based on our interpretation of results, which we identify as critical for the acceptance and usability of robots by an ageing society.

The efficient sharing of information is a commonly overlooked problem in methods proposed for cooperative multi-robot tasks. However, in multi-robot scenarios, especially when the communication network's quality of service is less than desirable, either in bandwidth or reliability, efficient information exchange is a key aspect for the successful deployment of coordinated robotic teams with proper exchange of information.