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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.

In this paper, a new approach to the generation and the role of artificial emotions in the decision-making process of autonomous agents (physical and virtual) is presented. The proposed decision-making system is biologically inspired and it is based on drives, motivations, and emotions. The agent has certain needs or drives that must be within a certain range, and motivations are understood as what moves the agent to satisfy a drive. Considering that the well-being of the agent is a function of its drives, the goal of the agent is to optimize it. Currently, the implemented artificial emotions are happiness, sadness, and fear. The novelties of our approach are, on one hand, that the generation method and the role of each of the artificial emotions are not defined as a whole, as most authors do. Each artificial emotion is treated separately. On the other hand, in the proposed system it is not mandatory to predefine either the situations that must release any artificial emotion or the actions that must be executed in each case. Both the emotional releaser and the actions can be learned by the agent, as happens on some occasions in nature, based on its own experience. In order to test the decision-making process, it has been implemented on virtual agents (software entities) living in a simple virtual environment. The results presented in this paper correspond to the implementation of the decision-making system on an agent whose main goal is to learn from scratch how to behave in order to maximize its well-being by satisfying its drives or needs. The learning process, as shown by the experiments, produces very natural results. The usefulness of the artificial emotions in the decision-making system is proven by making the same experiments with and without artificial emotions, and then comparing the performance of the agent. ; The authors gratefully acknowledge the funds provided by the Spanish Government through the project "A new approach to social robotics" (AROS), of MICINN (Ministry of Science and Innovation) and the 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.

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.

In this paper a decision making system for autonomous and social agents who live in a virtual world is presented. This world was built using a text based multi-user game: a MUD (Multi User Domain). In this world the agents can interact with one other, allowing social interaction, as well as interaction with the other objects present in the world. In this paper, the usefulness of using this kind of text based multi-user games as test beds for designing decision making systems of artificial agents, is proved. The proposed decision making system is composed of several subsystems: a mo-tivational system, a drives system and an evaluation and behaviour selection system. The selection of behaviours is learned by the agent using reinforcement learning al-gorithms. The dominant motivation is considered as the inner state of the agent. In order to simplify the learning process, the states related to the objects are considered as independent from one another. The state of the agent is a combination between his inner state and his state in relation with the rest of agents and objects. This system uses happiness and sadness, defined as positive and negative variations of the wellbeing of the agent, as the reinforcement function. ; The authors gratefully acknowledge the funds provided by the Spanish Government through the projects 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). ; Publicado

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.

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.

10 pages, 2 figures.-- Contributed to: Social Robotics - Second International Conference on Social Robotics (ICSR 2010, Singapore, Nov 23-24, 2010).-- Best Student Paper Finalist. ; 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 imitating humans' mind for taking decisions. Following this approach, we propose a motivational system for decision making using internal (drives) and external stimuli for choosing the right action. Actions will be selected from a finite set of skills in order to keep robot's needs within an acceptable range. ; We present how our motivational decision making is applied to a social robot showing an improvement in robot's autonomy. ; The authors gratefully acknowledge 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), and the project "A new approach to social robotics" (AROS), of MICINN (Ministry of Science and Innovation). ; Publicado

Lately, lots of effort has been put into the construction of robots able to live among humans. This fact has favored the development of personal or social robots, which are expected to behave in a natural way. This implies that these robots could meet certain requirements, for example, to be able to decide their own actions (autonomy), to be able to make deliberative plans (reasoning), or to be able to have an emotional behavior in order to facilitate human-robot interaction. In this paper, the authors present a bioinspired control architecture for an autonomous and social robot, which tries to accomplish some of these features. In order to develop this new architecture, authors have used as a base a prior hybrid control architecture (AD) that is also biologically inspired. Nevertheless, in the later, the task to be accomplished at each moment is determined by a fix sequence processed by the Main Sequencer. Therefore, the main sequencer of the architecture coordinates the previously programmed sequence of skills that must be executed. In the new architecture, the main sequencer is substituted by a decision making system based on drives, motivations, emotions, and self-learning, which decides the proper action at every moment according to robot's state. Consequently, the robot improves its autonomy since the added decision making system will determine the goal and consequently the skills to be executed. A basic version of this new architecture has been implemented on a real robotic platform. Some experiments are shown at the end of the paper. ; This work has been supported 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), the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid.

This paper presents a user localization system based on the fusion of visual information and sound source localization, implemented on a social robot called Maggie. One of the main requisites to obtain a natural interaction between human-human and human-robot is an adequate spatial situation between the interlocutors, that is, to be orientated and situated at the right distance during the conversation in order to have a satisfactory communicative process. Our social robot uses a complete multimodal dialog system which manages the user-robot interaction during the communicative process. One of its main components is the presented user localization system. To determine the most suitable allocation of the robot in relation to the user, a proxemic study of the human-robot interaction is required, which is described in this paper. The study has been made with two groups of users: children, aged between 8 and 17, and adults. Finally, at the end of the paper, experimental results with the proposed multimodal dialog system are presented. ; The authors gratefully acknowledge the funds provided by the Spanish Government through the project "A new approach to social robotics" (AROS), of MICINN (Ministry of Science and Innovation).

IEEE International Conference on Robotics and Automation, Leuven, Belgium, 1998 ; This paper presents a fuzzy perception planner that takes into account the time cost, the suitability of every landmark detection and the different uncertainties the robot encounters along its path for mobile robot localization. The sensor used is a camera with a motorized zoom on a pan & tilt platform and the artificial landmarks are circles detected through normalized grayscale correlation. An Extended Kalman Filter is used to correct the position and orientation of the vehicle. The resulting self-localization module has been integrated successfully in a more complicated navigation system. ; The authors gratefully acknowledge the funds provided by the Spanish Government through the CICYT projects TAP94-711 and TAP96-657, and thank B.J. Carabao and M.J. Lopez for their experimental work with the planner using the B2 1 robot. ; Publicado

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.