Editor’s Note

The research works presented in this issue are based on various topics of interest, among wich are included: Mobile services, gesture recognition, physics simulation, management decision support, business intelligence, Internet, remote executables, scientific computing, university-industry links, Sony AIBO, Aperios, toolchain, MAS, data fusion, tracks, merge, inference, Homeland Security, european projects, research trends, emerging technologies and desk research

Conectando el Robot AIBO a ROS: Extracción de imágenes

En este documento se realiza una introduccion al trabajo realizado con el robot AIBO, de Sony, para la obtencion de imagenes, asi como su tratamiento con el objetivo que el robot navegue de forma autonoma mediante algoritmos de vision artificial. A nivel practico, el principal punto de interes del articulo se centra en el entorno de programacion, que se ha exportado al marco de ROS (Robot Operating System), de forma que se pueda hacer uso de la extensa biblioteca de algoritmos ya desarrollados en este entorno.Postprint (published version

Community driven artificial intelligence development for robotics

AIBO was a brand of entertainment robots, but Sony discontinued in 2006. It was a very successful robot platform both in research labs and consumer market. An active community of enthusiastic AIBO owners is still around the world with their own repair services for hardware faults and damages what shows how strong is this platform. This PhD research attempts to create a special online community for building a new artificial intelligence (AI) software for Sony ERS-7 robots (Figure 1) as a case study to explore new ways of modern AI development.12th International Conference on Informatics in Control, Automation and Robotic

Different robotics platforms for different teaching needs

When facing the problem of teaching the basis of robot control programming to computer science students, apart from the syllabus of the course, some other requirements have to be taken into account. For instance, which are the most appropriate robotic platforms, and which are the best programming tools for teaching it. In this paper we describe the platforms and programming environments chosen for different senior and graduate robotic courses at Rey Juan Carlos University, focusing on the reasons under our choices. We also point out the practical assignments demanded on the courses. Finally, we will present the results along four years, the feedback from our students, and the lessons we have learne

Collaborative Artificial Intelligence Development for Social Robots

The main aim of this doctoral thesis was to investigate on how to involve a community for collaborative artificial intelligence (AI) development of a social robot. The work was initiated by the author’s personal interest in developing the Sony AIBO robots that have been unavailable on the retail markets, however, user communities with special interests in these robots remained on the internet. At first, to attract people’s attention, the author developed three specific features for the robot. These consisted of teaching the robot 1) sound event recognition in order to react to environmental audio stimuli, 2) a method to detect the underlying surface under the robot, and 3) of how to recognize its own body states. As this AI development proved to be very challenging, the author decided to start a community project for artificial intelligence development. Community involvement has a long history in open-source software projects and some robotics companies tried to benefit from their userbase in product development. An active online community of Sony AIBO owners was approached to investigate factors to engage its members in the creative processes. For this purpose, 78 Sony AIBO owners were recruited online to fill a questionnaire and their data were analyzed with respect to age, gender, culture, length of ownership, user contribution, and model preference. The results revealed the motives to own these robots for many years and how these heavy users perceived their social robots after a long period in the robot acceptance phase. For example, female participants tended to have more emotional relation to their robots than male who had more technically oriented long-term engagement motivation. The user expectations were also explored by analyzing the answers to this questionnaire to discover the key needs of this user group. The results revealed that the most-wanted skills were the interaction with humans and the autonomous operation. The integration with the AI agents and Internet services was important, but the long-term memory and learning capabilities were not so relevant for the participants. The diverse preferences for robot skills led to creating a prioritized recommendation list to complement the design guidelines for social robots in the literature. In sum, the findings of this thesis showed that developing AI features for an outdated robot is possible but takes a lot of time and shared community efforts. To involve a specific community, one needs first to build up trust by working with and for the community. Also, the trust for the long-term endurance of the development project was found as a precondition for the community commitment. The discoveries of this thesis can be applied to similar types of collaborative AI developments in the future. There are significant contributions in this dissertation to robotics. First, the long-term robot usage was not studied on a years-long scale before and the most extended human-robot interactions analyzed test subjects for only a few months. A questionnaire investigated the robot owners with 1-10+ years-long ownership in this work and their attitude towards robot acceptance. The survey results helped to understand the viable strategies to engage users for a long time. Second, innovative ways were explored to involve online communities in robotics development. The past approaches introduced the community ideas and opinions into product design and innovation iterations. The community in this dissertation tested the developed AI engine, provided inputs for further development directions, created content for the actual AI and gave their feedback about product quality. These contributions advance the social robotics field

Four-features evaluation of text to speech systems for three social robots

The success of social robotics is directly linked to their ability of interacting with people. Humans possess verbal and non-verbal communication skills, and, therefore, both are essential for social robots to get a natural human&-robot interaction. This work focuses on the first of them since the majority of social robots implement an interaction system endowed with verbal capacities. In order to do this implementation, we must equip social robots with an artificial voice system. In robotics, a Text to Speech (TTS) system is the most common speech synthesizer technique. The performance of a speech synthesizer is mainly evaluated by its similarity to the human voice in relation to its intelligibility and expressiveness. In this paper, we present a comparative study of eight off-the-shelf TTS systems used in social robots. In order to carry out the study, 125 participants evaluated the performance of the following TTS systems: Google, Microsoft, Ivona, Loquendo, Espeak, Pico, AT&T, and Nuance. The evaluation was performed after observing videos where a social robot communicates verbally using one TTS system. The participants completed a questionnaire to rate each TTS system in relation to four features: intelligibility, expressiveness, artificiality, and suitability. In this study, four research questions were posed to determine whether it is possible to present a ranking of TTS systems in relation to each evaluated feature, or, on the contrary, there are no significant differences between them. Our study shows that participants found differences between the TTS systems evaluated in terms of intelligibility, expressiveness, and artificiality. The experiments also indicated that there was a relationship between the physical appearance of the robots (embodiment) and the suitability of TTS systems.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; “RoboCity2030-DIH-CM”, funded by Comunidad de Madrid and co-funded by Structural Funds of the EU; “Robots Sociales para estimulación física, cognitiva y afectiva de mayores (ROSES)” funded by Agencia Estatal de Investigación (AEI).Publicad