An important aspect in Human–Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot’s shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot’s shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke, tap, slap, and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot’s shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot’s shell and plug it into the robot’s computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F-score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures.

The concept of joint attention holds significant importance in human interaction and is pivotal in establishing rapport, understanding, and effective communication. Within social robotics, enhancing user perception of the robot and promoting a sense of natural interaction with robots becomes a central element. In this sense, emulating human-centric qualities in social robots, such as joint attention, defined as the ability of two or more individuals to focus on a common event simultaneously, can increase their acceptability. This study analyses the impact on user perception of a responsive joint attention system integrated into a social robot within an interactive scenario. The experimental setup involves playing against the robot in the “Odds and Evens” game under two conditions: whether the joint attention system is active or inactive. Additionally, auditory and visual distractors are employed to simulate real-world distractions, aiming to test the system’s ability to capture and follow user attention effectively. To assess the influence of the joint attention system, participants completed the Robotic Social Attributes Scale (RoSAS) after each interaction. The results showed a significant improvement in user perception of the robot’s competence and warmth when the joint attention system was active.

La robótica social se encuentra ante el reto de diseñar robots que sean útiles para la sociedad, se puedan utilizar con frecuencia, y en los que la gente confie. Este problema se puede abordar desarrollando robots con alto grado de vinculación y compromiso con sus usuarios. Proponemos aplicar diversas estrategias relacionadas con la psicología social y la teoría de juegos en el campo de la robótica social. El objetivo es conseguir una vinculación entre el robot y sus usuarios, produciendo tiempos de interacción más largos para aumentar el uso del robot de manera diaria. La combinación de las diferentes estrategias se centra en desarrollar robots sociales que faciliten y promuevan la interacción con sus usuarios. Como novedad en este trabajo se incorporan estrategias de alto grado de vinculación en el campo de la robótica social. En concreto, para mejorar la utilización y el enganche en nuestro robot social Mini. Además, se muestran los resultados obtenidos en la validación de las estrategias propuestas en experimentos realizados por 21 participantes de avanzada edad. Estos resultados demuestran la utilidad de nuestro sistema para aumentar el tiempo de interacción con el robot durante ejercicios de entretenimiento.

Robots are becoming an increasingly important part of our society and have started to be used in tasks that require communicating with humans. Communication can be decoupled in two dimensions: symbolic (information aimed to achieve a particular goal) and spontaneous (displaying the speaker’s emotional and motivational state) communication. Thus, to enhance human–robot interactions, the expressions that are used have to convey both dimensions. This paper presents a method for modelling a robot’s expressiveness as a combination of these two dimensions, where each of them can be generated independently. This is the first contribution of our work. The second contribution is the development of an expressiveness architecture that uses predefined multimodal expressions to convey the symbolic dimension and integrates a series of modulation strategies for conveying the robot’s mood and emotions. In order to validate the performance of the proposed architecture, the last contribution is a series of experiments that aim to study the effect that the addition of the spontaneous dimension of communication and its fusion with the symbolic dimension has on how people perceive a social robot. Our results show that the modulation strategies improve the users’ perception and can convey a recognizable affective state.