Acoustic-based Smart Tactile Sensing in Social Robots

Mención Internacional en el título de doctorEl sentido del tacto es un componente crucial de la interacción social humana y es único entre los cinco sentidos. Como único sentido proximal, el tacto requiere un contacto físico cercano o directo para registrar la información. Este hecho convierte al tacto en una modalidad de interacción llena de posibilidades en cuanto a comunicación social. A través del tacto, podemos conocer la intención de la otra persona y comunicar emociones. De esta idea surge el concepto de social touch o tacto social como el acto de tocar a otra persona en un contexto social. Puede servir para diversos fines, como saludar, mostrar afecto, persuadir y regular el bienestar emocional y físico. Recientemente, el número de personas que interactúan con sistemas y agentes artificiales ha aumentado, principalmente debido al auge de los dispositivos tecnológicos, como los smartphones o los altavoces inteligentes. A pesar del auge de estos dispositivos, sus capacidades de interacción son limitadas. Para paliar este problema, los recientes avances en robótica social han mejorado las posibilidades de interacción para que los agentes funcionen de forma más fluida y sean más útiles. En este sentido, los robots sociales están diseñados para facilitar interacciones naturales entre humanos y agentes artificiales. El sentido del tacto en este contexto se revela como un vehículo natural que puede mejorar la Human-Robot Interaction (HRI) debido a su relevancia comunicativa en entornos sociales. Además de esto, para un robot social, la relación entre el tacto social y su aspecto es directa, al disponer de un cuerpo físico para aplicar o recibir toques. Desde un punto de vista técnico, los sistemas de detección táctil han sido objeto recientemente de nuevas investigaciones, sobre todo dedicado a comprender este sentido para crear sistemas inteligentes que puedan mejorar la vida de las personas. En este punto, los robots sociales se han convertido en dispositivos muy populares que incluyen tecnologías para la detección táctil. Esto está motivado por el hecho de que un robot puede esperada o inesperadamente tener contacto físico con una persona, lo que puede mejorar o interferir en la ejecución de sus comportamientos. Por tanto, el sentido del tacto se antoja necesario para el desarrollo de aplicaciones robóticas. Algunos métodos incluyen el reconocimiento de gestos táctiles, aunque a menudo exigen importantes despliegues de hardware que requieren de múltiples sensores. Además, la fiabilidad de estas tecnologías de detección es limitada, ya que la mayoría de ellas siguen teniendo problemas tales como falsos positivos o tasas de reconocimiento bajas. La detección acústica, en este sentido, puede proporcionar un conjunto de características capaces de paliar las deficiencias anteriores. A pesar de que se trata de una tecnología utilizada en diversos campos de investigación, aún no se ha integrado en la interacción táctil entre humanos y robots. Por ello, en este trabajo proponemos el sistema Acoustic Touch Recognition (ATR), un sistema inteligente de detección táctil (smart tactile sensing system) basado en la detección acústica y diseñado para mejorar la interacción social humano-robot. Nuestro sistema está desarrollado para clasificar gestos táctiles y localizar su origen. Además de esto, se ha integrado en plataformas robóticas sociales y se ha probado en aplicaciones reales con éxito. Nuestra propuesta se ha enfocado desde dos puntos de vista: uno técnico y otro relacionado con el tacto social. Por un lado, la propuesta tiene una motivación técnica centrada en conseguir un sistema táctil rentable, modular y portátil. Para ello, en este trabajo se ha explorado el campo de las tecnologías de detección táctil, los sistemas inteligentes de detección táctil y su aplicación en HRI. Por otro lado, parte de la investigación se centra en el impacto afectivo del tacto social durante la interacción humano-robot, lo que ha dado lugar a dos estudios que exploran esta idea.The sense of touch is a crucial component of human social interaction and is unique among the five senses. As the only proximal sense, touch requires close or direct physical contact to register information. This fact makes touch an interaction modality full of possibilities regarding social communication. Through touch, we are able to ascertain the other person’s intention and communicate emotions. From this idea emerges the concept of social touch as the act of touching another person in a social context. It can serve various purposes, such as greeting, showing affection, persuasion, and regulating emotional and physical well-being. Recently, the number of people interacting with artificial systems and agents has increased, mainly due to the rise of technological devices, such as smartphones or smart speakers. Still, these devices are limited in their interaction capabilities. To deal with this issue, recent developments in social robotics have improved the interaction possibilities to make agents more seamless and useful. In this sense, social robots are designed to facilitate natural interactions between humans and artificial agents. In this context, the sense of touch is revealed as a natural interaction vehicle that can improve HRI due to its communicative relevance. Moreover, for a social robot, the relationship between social touch and its embodiment is direct, having a physical body to apply or receive touches. From a technical standpoint, tactile sensing systems have recently been the subject of further research, mostly devoted to comprehending this sense to create intelligent systems that can improve people’s lives. Currently, social robots are popular devices that include technologies for touch sensing. This is motivated by the fact that robots may encounter expected or unexpected physical contact with humans, which can either enhance or interfere with the execution of their behaviours. There is, therefore, a need to detect human touch in robot applications. Some methods even include touch-gesture recognition, although they often require significant hardware deployments primarily that require multiple sensors. Additionally, the dependability of those sensing technologies is constrained because the majority of them still struggle with issues like false positives or poor recognition rates. Acoustic sensing, in this sense, can provide a set of features that can alleviate the aforementioned shortcomings. Even though it is a technology that has been utilised in various research fields, it has yet to be integrated into human-robot touch interaction. Therefore, in thiswork,we propose theATRsystem, a smart tactile sensing system based on acoustic sensing designed to improve human-robot social interaction. Our system is developed to classify touch gestures and locate their source. It is also integrated into real social robotic platforms and tested in real-world applications. Our proposal is approached from two standpoints, one technical and the other related to social touch. Firstly, the technical motivation of thiswork centred on achieving a cost-efficient, modular and portable tactile system. For that, we explore the fields of touch sensing technologies, smart tactile sensing systems and their application in HRI. On the other hand, part of the research is centred around the affective impact of touch during human-robot interaction, resulting in two studies exploring this idea.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Pedro Manuel Urbano de Almeida Lima.- Secretaria: María Dolores Blanco Rojas.- Vocal: Antonio Fernández Caballer

Corrección de odometría empleando visual servoing en ROS

Los avances que se están realizando actualmente en el campo de los vehículos autónomos y la navegación no tripulada se hacen cada vez más patentes en nuestra sociedad. Desde las pequeñas incorporaciones que están haciendo las grandes marcas en sus modelos, como el aparcamiento asistido o la previsión de colisiones, hasta los pioneros en vehículos totalmente autónomos como son Google o Uber, la autonomía de los vehículos se perfila como una de los grandes avances de este tiempo. Algunos de los elementos que motivan este avance son la búsqueda del descenso de los accidentes en carretera, la reducción de la contaminación ambiental y el acceso a cualquier persona a un vehículo propio. La Visión por Computador juega un papel fundamental en el avance de la autonomía en los vehículos, dotándolos de un gran flujo de información. Además, gracias a los últimos avances en sistemas de percepción, la fiabilidad de esta información se ha incrementado considerablemente. Hasta ahora el acceso o la contribución en todo este tipo de tecnología estaba al alcance de muy pocos, pero con la aparición y creciente desarrollo de entornos de programación asociados a la robótica como ROS, basados en el código libre, estas barreras han desaparecido, permitiendo que se creen grandes comunidades de desarrolladores, que en definitiva contribuyen en una gran medida al ya vertiginoso avance de la robótica. El proyecto propuesto consiste en crear un sistema que sirva para reiniciar el error que se genera en un sistema odométrico que controla el posicionamiento de un carrito de golf contribuyendo a su autonomía. El sistema se desarrollará en el entorno de programación conocido como ROS, empleando técnicas de Visión por Computador, específicamente de Visual Servoing, y siendo programado empleando el lenguaje de programación C++.The progress that is currently being made in the field of autonomous vehicles and drone navigation are becoming more visible in our society. From small additions that are being introduced in some models of the big brands, such as the park-assist system or the collision forecast system, to the pioneers in completely autonomous vehicles like Google or Uber, vehicle autonomy is emerging as one of the major advances in this time. Some of the motivations for this advance are the pursuit of the drop in road accidents, reducing the pollution and the access to any individual to their own vehicle. Computer vision plays a fundamental role in the progress of the autonomy in vehicles, providing them with a large flow of information. Besides, due to the latest developments in perception systems, the reliability of this information has increased considerably. Until now, access or contribution to all this technology, was available to only a few people, but with the emergence and the development of programming environments associated to robotics such as ROS, based on open source code, all this obstacles have disappeared, allowing large communities of developers, which contribute to a great extent to the vertiginous advance of robotics. The proposed project is to create a system that serves to reset the error that is generated in an odometer system that controls the positioning of an golf cart, contributing to its autonomy. The system will be developed in the programming environment known as ROS, using computer vision techniques, specifically Visual Sevoing, and being programmed using the programming language C++.Ingeniería Electrónica Industrial y Automátic

Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning

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 research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU.Publicad

Asynchronous federated learning system for human-robot touch interaction

Artificial intelligence and robotics are advancing at an incredible pace; however, there is a risk associated with the data privacy and personal information of users interacting with these systems and platforms. In this context, the federated learning approach emerged to enable large-scale, distributed learning without the need to transmit or store any information necessary to train the learning models. In a previous paper, we presented a system capable of detecting, locating, and classifying what kind of contact occurs between humans and one of our robots using innovative contact microphone technology. In this work we go further, improving the previously presented touch system with a multi-user, multi-robot, distributed, and scalable learning approach that is able to learn in a collaborative and incremental way while respecting the privacy of the user's information. The system has been successfully evaluated in a real environment with 28 different users divided in 7 different groups. To assess the performance of our system with this federated learning approach, we compared it to the same distributed learning system without federated learning. That is, the control group for this comparison is a central node directly receiving all the training examples obtained by each robot locally. We found that in this context the inclusion of federated learning improves the results concerning traditional distributed learning.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación; the project PLEC2021-007819, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR, and RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by the European Social Funds (FSE) of the EU. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022 )

Detecting, locating and recognising human touches in social robots with contact microphones

There are many situations in our daily life where touch gestures during natural human–human interaction take place: meeting people (shaking hands), personal relationships (caresses), moments of celebration or sadness (hugs), etc. Considering that robots are expected to form part of our daily life in the future, they should be endowed with the capacity of recognising these touch gestures and the part of its body that has been touched since the gesture’s meaning may differ. Therefore, this work presents a learning system for both purposes: detect and recognise the type of touch gesture (stroke, tickle, tap and slap) and its localisation. The interpretation of the meaning of the gesture is out of the scope of this paper. Different technologies have been applied to perceive touch by a social robot, commonly using a large number of sensors. Instead, our approach uses 3 contact microphones installed inside some parts of the robot. The audio signals generated when the user touches the robot are sensed by the contact microphones and processed using Machine Learning techniques. We acquired information from sensors installed in two social robots, Maggie and Mini (both developed by the RoboticsLab at the Carlos III University of Madrid), and a real-time version of the whole system has been deployed in the robot Mini. The system allows the robot to sense if it has been touched or not, to recognise the kind of touch gesture, and its approximate location. The main advantage of using contact microphones as touch sensors is that by using just one, it is possible to “cover” a whole solid part of the robot. Besides, the sensors are unaffected by ambient noises, such as human voice, TV, music etc. Nevertheless, the fact of using several contact microphones makes possible that a touch gesture is detected by all of them, and each may recognise a different gesture at the same time. The results show that this system is robust against this phenomenon. Moreover, the accuracy obtained for both robots is about 86%.The research leading to these results has received funding from the projects: ‘‘Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES)’’, funded by the Spanish "Ministerio de Ciencia, Innovación y Universidades, Spain" and from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by ‘"Programas de Actividades I+D en la Comunidad de Madrid’" and cofunded by Structural Funds of the EU, Slovak Republic.Publicad

Mini: A New Social Robot for the Elderly

The unceasing aging of the population is leading to new problems in developed countries. Robots represent an opportunity to extend the period of independent living of the elderly as well as to ameliorate their economic burden and social problems. We present a new social robot, Mini, specifically designed to assist and accompany the elderly in their daily life either at home or in a nursing facility. Based on the results of several meetings with experts in this field, we have built a robot able to provide services in the areas of safety, entertainment, personal assistance and stimulation. Mini supports elders and caregivers in cognitive and mental tasks. We present the robot platform and describe the software architecture, particularly focussing on the human–robot interaction. We give in detail how the robot operates and the interrelation of the different modules of the robot in a real use case. In the last part of the paper, we evaluated how users perceive the robot. Participants reported interesting results in terms of usability, appearance, and satisfaction. This paper describes all aspects of the design and development of a new social robot that can be used by other researchers who face the multiple challenges of creating a new robotic platform for older people.The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economía y Competitividad; and Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), funded by the Ministerio de Ciencia, Innovación y Universidades.Publicad

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

A Bio-Inspired Endogenous Attention-Based Architecture for a Social Robot

A robust perception system is crucial for natural human&ndash;robot interaction. An essential capability of these systems is to provide a rich representation of the robot&rsquo;s environment, typically using multiple sensory sources. Moreover, this information allows the robot to react to both external stimuli and user responses. The novel contribution of this paper is the development of a perception architecture, which was based on the bio-inspired concept of endogenous attention being integrated into a real social robot. In this paper, the architecture is defined at a theoretical level to provide insights into the underlying bio-inspired mechanisms and at a practical level to integrate and test the architecture within the complete architecture of a robot. We also defined mechanisms to establish the most salient stimulus for the detection or task in question. Furthermore, the attention-based architecture uses information from the robot&rsquo;s decision-making system to produce user responses and robot decisions. Finally, this paper also presents the preliminary test results from the integration of this architecture into a real social robot

Applying psychological and social strategies to increase engagement in human-robot interaction

[EN] Social robotics faces the challenge of designing robots that are useful to society, can be used frequently, and that people trust. This problem can be addressed by developing robots with a high degree of bonding and engagement with their users. We propose to apply various strategies related to social psychology and game theory in the field of social robotics. The goal is to achieve bonding between the robot and its users, producing longer interaction times to increase the use of the robot on a daily basis. The combination of the different strategies focuses on developing social robots that facilitate and promote interaction with their users. As a novelty in this work, psychological strategies are incorporated in the field of social robotics. Specifically, to improve the utilization and engagement in our Mini social robot. Besides, we show the results obtained in the validation of the method in experiments performed by 21 elderly participants. These results demonstrate the usefulness of our system to increase the interaction time with the robot during entertainment sessions.[ES] 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.Esta investigación ha sido apoyada por los siguientes proyectos: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, financiado por el Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, financiado por la Agencia Estatal de Investigación (AEI), Ministerio de Ciencia e Innovación de España; el proyecto PLEC2021-007819, financiado por MCIN/AEI/10.13039/501100011033 y por la Union Europea Next- GenerationEU/PRTR, y RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiado por “Programas de Actividades I+D en la Comunidad de Madrid” y cofinanciado por los European Social Funds (FSE) de la Unión Europea.Carrasco Martínez, S.; Gamboa Montero, JJ.; Maroto Gómez, M.; Alonso Martín, F.; Salichs, MÁ. (2023). Aplicación de estrategias psicológicas y sociales para incrementar el vínculo en interacción humano-robot. Revista Iberoamericana de Automática e Informática industrial. 20(2):199-212. https://doi.org/10.4995/riai.2023.1873919921220

Epidemiology of caprine arthritis encephalitis in Costa Rican dairy goat herds

El objetivo de este trabajo fue determinar aspectos epidemiológicos tales como seroprevalencia, incidencia y factores de riesgo asociados a la transmisión del virus de la artritis-encefalitis caprina (CAEV) en hatos caprinos de pie de cría de Costa Rica. Entre julio del 2005 y junio del 2006, se recolectó muestras de sangre de 340 cabras en 11, de un total de 15, hatos de pie de cría registrados y activos en la Asociación Costarricense de Criadores de Cabras, distribuidos en: San José, Heredia, Cartago, Alajuela y Puntarenas. Al momento del muestreo, se aplicó un cuestionario a los productores para conocer las prácticas de manejo que pueden estar relacionadas con la transmisión del virus. Las muestras fueron analizadas mediante un ELISA competitivo, para detectar anticuerpos contra CAEV. La seroprevalencia global determinada fue de 61,2% (IC 95%: 56,0–66,4), con un rango de 0,0 a 98,4% a nivel de finca. La incidencia acumulativa fue de 32,9% (IC 95%: 25,6–40,2), para un período de seguimiento de 6 meses. Se observó una frecuencia importante de prácticas de manejo que favorecen la transmisión del virus y refuerzan la infección, tales como: trabajar en un sistema de hato abierto (OR = 3,64; IC: 2,34-5,66), uso del macho en monta natural sin distingo del estatus serológico (OR= 3,55; IC: 2,82-4,46), la existencia de casos clínicos de CAEV (OR= 3,64; IC: 2,34-5,66), no desinfectar la areteadora una vez utilizada entre animales (OR= 2,30; IC:1,79-2,96), y el uso de leche de cabras con mastitis en la cría de las cabritas de reemplazo (OR= 2,04; IC: 1,53-2,72). Las condiciones de manejo, determinadas en los hatos caprinos estudiados, posibilitan la transmisión del virus; por consiguiente, es altamente probable el mantenimiento de la infección. De esta manera, el control de la enfermedad está muy lejos de lograrse y, probablemente, sus efectos seguirán impactando los hatos en forma negativa.The objective of the study was to determine epidemiological aspects, such as seroprevalence, incidence and risk factors associated to the transmission of the caprine arthritis encephalitis virus (CAEV) in dairy goat breeding herds in Costa Rica. Blood samples were collected between July 2005 and June 2006 from 340 goats in 11 out of 15 goat breeding herds, actively registered with the Costa Rican Association of Goat Breeders (Asociación Costarricense de Criadores de Cabras), located in San José, Heredia, Cartago, Alajuela and Puntarenas. At the time of sampling, a questionnaire was applied to breeders to know how virus transmission practices are handled. Blood samples were analyzed using a competitive ELISA to detect antibodies against CAEV. Overall seroprevalence was 61.2% (CI 95%: 56.0-66.4), ranging between 0.0 and 98.4% at farm level, while cumulative incidence was 32.9% (CI 95%: 25.6-40.2) for a follow up period of 6 months. Significant frequency is observed in management practices that favor the transmission of the virus and reinforce the infection, such as working in an open herding system (OR = 3.64; CI: 2.34-5.66), using the male in natural mating regardless of its serostatus (OR = 3.55; CI: 2.82-4.46), having clinical cases of CAEV (OR = 3.64; CI: 2.34-5.66), not disinfecting the ear tag applicator between animals (OR = 2.30; CI:1.79-2.96), and using milk from goats with mastitis to feed newborns (OR = 2.04; CI: 1.53-2.72). Management conditions found in the studied goat herds favor transmission of the virus, making infection very likely to remain. Therefore, controlling this disease is far from being achieved and, most probable, its effects will continue to have a negative impact on dairy goat herds.Universidad Nacional, Costa RicaEscuela de Medicina Veterinari