Integration of a voice recognition system in a social robot

Human-Robot Interaction (HRI) 1 is one of the main fields in the study and research of robotics. Within this field, dialog systems and interaction by voice play a very important role. When speaking about human- robot natural dialog we assume that the robot has the capability to accurately recognize the utterance what the human wants to transmit verbally and even its semantic meaning, but this is not always achieved. In this paper we describe the steps and requirements that we went through in order to endow the personal social robot Maggie, developed in the University Carlos III of Madrid, with the capability of understanding the natural language spoken by any human. We have analyzed the different possibilities offered by current software/hardware alternatives by testing them in real environments. We have obtained accurate data related to the speech recognition capabilities in different environments, using the most modern audio acquisition systems and analyzing not so typical parameters as user age, sex, intonation, volume and language. Finally we propose a new model to classify recognition results as accepted and rejected, based in a second ASR opinion. This new approach takes into account the pre-calculated success rate in noise intervals for each recognition framework decreasing false positives and false negatives rate.The funds have provided by the Spanish Government through the project called `Peer to Peer Robot-Human Interaction'' (R2H), of MEC (Ministry of Science and Education), and the project “A new approach to social robotics'' (AROS), of MICINN (Ministry of Science and Innovation). The research leading to these results has received funding from the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Learning to Avoid Risky Actions

When a reinforcement learning agent executes actions that can cause frequent damage to itself, it can learn, by using Q-learning, that these actions must not be executed again. However, there are other actions that do not cause damage frequently but only once in a while, for example, risky actions such as parachuting. These actions may imply punishment to the agent and, depending on its personality, it would be better to avoid them. Nevertheless, using the standard Q-learning algorithm, the agent is not able to learn to avoid them, because the result of these actions can be positive on average. In this article, an additional mechanism of Q-learning, inspired by the emotion of fear, is introduced in order to deal with those risky actions by considering the worst results. Moreover, there is a daring factor for adjusting the consideration of the risk. This mechanism is implemented on an autonomous agent living in a virtual environment. The results present the performance of the agent with different daring degrees.The funds provided by the Spanish Government through the project called “A New Approach to Social Robotics” (AROS), of MICINN (Ministry of Science and Innovation) and through the RoboCity2030-IICM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

End-user programming of a social robot by dialog

One of the main challenges faced by social robots is how to provide intuitive, natural and enjoyable usability for the end-user. In our ordinary environment, social robots could be important tools for education and entertainment (edutainment) in a variety of ways. This paper presents a Natural Programming System (NPS) that is geared to non-expert users. The main goal of such a system is to provide an enjoyable interactive platform for the users to build different programs within their social robot platform. The end-user can build a complex net of actions and conditions (a sequence) in a social robot via mixed-initiative dialogs and multimodal interaction. The system has been implemented and tested in Maggie, a real social robot with multiple skills, conceived as a general HRI researching platform. The robot's internal features (skills) have been implemented to be verbally accessible to the end-user, who can combine them into others that are more complex following a bottom-up model. The built sequence is internally implemented as a Sequence Function Chart (SFC), which allows parallel execution, modularity and re-use. A multimodal Dialog Manager System (DMS) takes charge of keeping the coherence of the interaction. This work is thought for bringing social robots closer to non-expert users, who can play the game of "teaching how to do things" with the robot.The research leading to these results has received funding from the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU. The authors also gratefully acknowledge the funds provided by the Spanish Ministry of Science and Innovation (MICINN) through the project named “A New Approach to Social Robots” (AROS) DPI2008-01109

Fast 3D cluster tracking for a mobile robot using 2D techniques on depth images

User simultaneous detection and tracking is an issue at the core of human-robot interaction (HRI). Several methods exist and give good results; many use image processing techniques on images provided by the camera. The increasing presence in mobile robots of range-imaging cameras (such as structured light devices as Microsoft Kinects) allows us to develop image processing on depth maps. In this article, a fast and lightweight algorithm is presented for the detection and tracking of 3D clusters thanks to classic 2D techniques such as edge detection and connected components applied to the depth maps. The recognition of clusters is made using their 2D shape. An algorithm for the compression of depth maps has been specifically developed, allowing the distribution of the whole processing among several computers. The algorithm is then applied to a mobile robot for chasing an object selected by the user. The algorithm is coupled with laser-based tracking to make up for the narrow field of view of the range-imaging camera. The workload created by the method is light enough to enable its use even with processors with limited capabilities. Extensive experimental results are given for verifying the usefulness of the proposed method.Spanish MICINN (Ministry of Science and Innovation) through the project ‘‘Applications of Social Robots=Aplicaciones de los Robots Sociales.’’Publicad

Remote interaction with mobile robots

This paper describes an architecture, which can be used to build remote laboratories to interact remotely via Internet with mobile robots using different interaction devices. A supervisory control strategy has been used to develop the remote laboratory in order to alleviate high communication data rates and system sensitivity to network delays. The users interact with the remote system at a more abstract level using high level commands. The local robot's autonomy has been increased by encapsulating all the robot's behaviors in different types of skills. User interfaces have been designed using visual proxy pattern to facilitate any future extension or code reuse. The developed remote laboratory has been integrated into an educational environment in the field of indoor mobile robotics. This environment is currently being used as a part of an international project to develop a distributed laboratory for autonomous and teleoperated systems (IECAT, 2003).Publicad

Learning the selection of actions for an autonomous social robot by reinforcement learning based on motivations

Autonomy is a prime issue on robotics field and it is closely related to decision making. Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions. Following this approach, we propose a motivational system for decision making, using internal (drives) and external stimuli for learning to choose the right action. Actions are selected from a finite set of skills in order to keep robot's needs within an acceptable range. The robot uses reinforcement learning in order to calculate the suitability of every action in each state. The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environment. The used reinforcement learning method exploits a new algorithm called Object Q-Learning. The proposed reduction of the state space and the new algorithm considering the collateral effects (relationship between different objects) results in a suitable algorithm to be applied to robots living in real environments. In this paper, a first implementation of the decision making system and the learning process is implemented on a social robot showing an improvement in robot's performance. The quality of its performance will be determined by observing the evolution of the robot's wellbeing.The funds provided by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), and the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

An autonomous social robot in fear

Currently artificial emotions are being extensively used in robots. Most of these implementations are employed to display affective states. Nevertheless, their use to drive the robot's behavior is not so common. This is the approach followed by the authors in this work. In this research, emotions are not treated in general but individually. Several emotions have been implemented in a real robot, but in this paper, authors focus on the use of the emotion of fear as an adaptive mechanism to avoid dangerous situations. In fact, fear is used as a motivation which guides the behavior during specific circumstances. Appraisal of fear is one of the cornerstones of this work. A novel mechanism learns to identify the harmful circumstances which cause damage to the robot. Hence, these circumstances elicit the fear emotion and are known as fear releasers. In order to prove the advantages of considering fear in our decision making system, the robot's performance with and without fear are compared and the behaviors are analyzed. The robot's behaviors exhibited in relation to fear are natural, i.e., the same kind of behaviors can be observed on animals. Moreover, they have not been preprogrammed, but learned by real interactions in the real world. All these ideas have been implemented in a real robot living in a laboratory and interacting with several items and people.The funds have been provided by the Spanish Government through the project called "A new approach to social robotics" (AROS), of MICINN (Ministry of Science and Innovation) and through the RoboCity2030- II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Signage System for the Navigation of Autonomous Robots in Indoor Environments

In many occasions people need to go to certain places without having any prior knowledge about the environment. This situation may occur when the place is visited for the first time, or even when there is not any available map to situate us. In those cases, the signs of the environment are essential for achieving the goal. The same situation may happen for an autonomous robot. This kind of robots must be capable of solving this problem in a natural way. In order to do that, they must use the resources present in their environment. This paper presents a RFID-based signage system, which has been developed to guide and give important information to an autonomous robot. The system has been implemented in a real indoor environment and it has been successfully proved in the autonomous and social robot Maggie. At the end of the paper some experimental results, carried out inside our university building, are presented.Comunidad de Madri

Study of scenarios and technical requirements of a social assistive robot for Alzheimer's disease patients and their caregivers

Robots have begun to assist elders and patients suffering dementia. In particular, recent studies have shown how robots can benefit Alzheimer's disease (AD) patients. This is a novel area with a promising future but lot of researching needs to be done. The RobAlz project is aimed to assist AD patients and their caregivers by social robots. This project is divided in three phases: the definition of the requirements and scenarios, the development of a new robotic platform, and the evaluation. This work presents the results obtained in the first phase, in which several meetings were conducted with a set of subject-matter experts in the areas of Alzheimer's Disease and social robotics. The meetings were classified according to the application areas they covered: general aspects, safety, entertainment, personal assistance, and stimulation. The meetings ended up with a repertory of scenarios where robots can be applied to Alzheimer's patients and their caregivers at their home or in longterm care facilities. These scenarios present different psychological, social and technical concerns that must be addressed for the design of the robot. In this work we perform an analysis on the scenarios and present the technical requirements for the development of a first robotic prototype. This prototype will be constructed and tested in real environments in the subsequent phases of the RobAlz project.The authors gratefully acknowledge the collaboration of the Spanish Alzheimer Foundation (FAE) and the funds provided by the Spanish Government through the project Aplicaciones de los robots sociales DPI2011-26980, from the Spanish Ministry of Economy and Competitiveness

Toma de decisiones en robótica

En este artículo se presenta, en forma de tutorial, una visión de conjunto de la situación actual del problema de la toma de decisiones en robótica. El estudio se plantea de forma amplia e integradora y, por tanto, intentando no entrar en detallar soluciones concretas para problemas también concretos. El artículo está centrado sobre todo en las decisiones de alto nivel que debe tomar un robot, y no en problemas de más bajo nivel, que se solucionan empleando técnicas tradicionales de control o mediante algoritmos muy específicos. Nos referimos a "toma de decisiones" de un robot en el sentido amplio de determinar las actividades a realizar por el robot. Es decir, sin hacer ninguna exclusión a priori, basada en cuestiones tales como la estrategia de toma de decisiones empleada, el tipo de robot, las tareas que puede realizar, etc. El artículo está estructurado en una serie de secciones, en las que se tratan diversos temas de interés en robótica, desde la perspectiva de la toma de decisiones: autonomía, inteligencia, objetivos, decisiones de alto nivel, estrategias de toma de decisiones, arquitecturas de control, percepción, interacción humano-robot, aprendizaje y emociones.Este trabajo ha sido realizado parcialmente gracias al apoyo económico del Gobierno de España a través del proyecto “Interacción igual a igual humano-robot (R2H)”, del Ministerio de Educación y Ciencia, y del proyecto “Una nueva aproximación a los robots sociales (AROS)”, del Ministerio de Ciencia e Innovación. Este trabajo ha sido financiado por la Comnidad de Madrid ( S2009/DPI-1559/ROBOCITY2030 II), desarrollado por el Laboratorio de Robótica (Robotics Lab)de laUniversidad Carlos III de MadridPublicad