Jesús García-Martínez

The way robots represent the environment, make decisions, and express themselves can positively influence human–robot interaction if they clearly communicate their intentions and needs. To improve human–robot communication, biologically inspired models that mimic human communication skills, including task and scenario-specific contextual information, can facilitate mutual understanding and successful task execution. This paper presents a Context-Awareness and Biologically Inspired Behaviour system to generate a more natural human–robot interaction. The architecture combines sensory information processed by a Joint Attention System that prioritises stimuli based on internal processes with task-related motivations to generate context- and goal-adapted verbal and non-verbal interaction. We evaluate the system through a video-based user study that compares two robots with similar appearances but different behaviours, one using the proposed approach and the other not using the internal state and joint attention mechanisms, to make verbal and non-verbal responses. The results show that participants rated the robot endowed with the proposed system as significantly more sociable, agentic, and animated than the robot without it. Additionally, the robot not showing the responses developed in this work was perceived as more disturbing than the robot integrating the proposed system.

Autonomous social robots aim to reduce human supervision by performing various tasks. To achieve this, they are equipped with multiple perceptual channels to interpret and respond to environmental cues in real time. However, multimodal perception often leads to sensory overload, as robots may receive numerous simultaneous stimuli with varying durations or persistent activations across different sensory modalities. Sensor overstimulation and false positives can compromise a robot’s ability to prioritise relevant inputs, sometimes resulting in repeated or inaccurate behavioural responses that reduce the quality and coherence of the interaction. This paper presents a Bioinspired Attentional System that uses Reinforcement Learning to manage stimulus prioritisation in real time. The system draws inspiration from the following two neurocognitive mechanisms: Inhibition of Return, which progressively reduces the importance of previously attended stimuli that remain active over time, and Attentional Fatigue, which penalises stimuli of the same perception modality when they appear repeatedly or simultaneously. These mechanisms define the algorithm’s reward function to dynamically adjust the weights assigned to each stimulus, enabling the system to select the most relevant one at each moment. The system has been integrated into a social robot and tested in three representative case studies that show how it modulates sensory signals, reduces the impact of redundant inputs, and improves stimulus selection in overstimulating scenarios. Additionally, we compare the proposed method with a baseline where the robot executes expressions as soon as it receives them using a queue. The results show the system’s significant improvement in expression management, reducing the number of expressions in the queue and the delay in performing them.

Los robots mascotas están diseñados para fomentar vínculos afectivos a través del contacto físico, lo que los convierte en plataformas especialmente sensibles a estímulos táctiles y de movimiento. Por tanto, esta riqueza sensorial puede generar situaciones de sobreestimulación cuando múltiples sensores se activan de forma simultánea, provocando respuestas incoherentes por parte del robot. En este artículo abordamos esta problemática presentando el Sistema de Atención Bioinspirado (SABi), una arquitectura de percepción diseñada para gestionar y priorizar dinámicamente estímulos en tiempo real. El SABi se inspira en dos mecanismos cognitivos humanos: la inhibición de retorno, que reduce la importancia de los estímulos previamente atendidos, y la fatiga atencional, que penaliza la aparición repetida de estímulos de la misma tipología. Dicho módulo constituye el sistema de percepción del robot social Mia, permitiéndole seleccionar el estímulo más relevante en cada momento en función de un ranking de prioridades. Los resultados obtenidos en un escenario real de interacción muestran que el SABi mejora la capacidad del robot para filtrar señales redundantes y responder de forma coherente al contexto afectivo, favoreciendo una interacción más fluida y natural.

Joint attention, the capacity of two or more individuals to focus on a common event simultaneously, is fundamental to human–human interaction, enabling effective communication. When considering the field of social robotics, emulating this capability might be necessary for promoting natural interactions and thus improving user engagement. Responding to joint attention (RJA), defined as the ability to react to external attentional cues by aligning focus with another individual, plays a critical role in promoting mutual understanding. This study examines how RJA impacts user engagement during human–robot interaction. The participants play a turn-taking game against a social robot under two conditions: with our RJA system active and with the system inactive. Auditory and visual stimuli are introduced to simulate real-world dynamics, testing the robot’s ability to detect and follow the user’s focus of attention. We use a twofold approach to evaluate the system’s impact on the user’s experience during the interaction. On the one hand, we use head pose telemetry to quantify attentional aspects of engagement, including measures of distraction and focus during the interaction. On the other hand, we use a post-experimental questionnaire incorporating the User Engagement Scale Short Form to assess engagement. The results regarding telemetry data reveal reduced distraction and improved attentional consistency, highlighting the system’s ability to maintain attention on the current task effectively. Furthermore, the questionnaire responses show that RJA significantly enhances self-reported engagement when the system is active. We believe these findings confirm the value of attentional mechanisms in promoting engaging human–robot interactions.

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 and fostering 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 simultaneously focus on a common event, can increase their acceptability. This work introduces a responsive joint attention system integrated into the social robot Mini. The system calculates the position of the user's head, body, and face orientations in real‐time. With this information, the robot can perform natural movements based on the user's gaze and pointing directions. Moreover, the robot employs verbal expressions to alert the user when distractions are detected, enhancing the perception of the robot's responsiveness. This study also explores differences in user perception of the robot when the joint attention system is active. A user experiment involving an interactive gaming scenario is proposed. The results show significance in various social presence dimensions, validating the system and indicating that users discern when the joint attention system is active and perceive the robot's responsive behavior.