Dynamaid, čovjekoliki robot za istraživanje uslužnih djelatnosti u kućanstvima

Domestic tasks require three main skills from autonomous robots: robust navigation, object manipulation, and intuitive communication with the users. Most robot platforms, however, support only one or two of the above skills. In this paper we present Dynamaid, a robot platform for research on domestic service applications. For robust navigation, Dynamaid has a base with four individually steerable differential wheel pairs, which allow omnidirectional motion. For mobile manipulation, Dynamaid is additionally equipped with two anthropomorphic arms that include a gripper, and with a trunk that can be lifted as well as twisted. For intuitive multimodal communication, the robot has a microphone, stereo cameras, and a movable head. Its humanoid upper body supports natural interaction. It can perceive persons in its environment, recognize and synthesize speech. We developed software for the tests of the RoboCup@Home competitions, which serve as benchmarks for domestic service robots. With Dynamaid and our communication robot Robotinho, our team Nimbro@Home took part in the RoboCup German Open 2009 and RoboCup 2009 competitions in which we came in second and third, respectively. We also won the innovation award for innovative robot design, empathic behaviors, and robot-robot cooperation.Tri glavne vještine koje se nameću autonomnim robotima pri obavljanju kućanskih zadataka su: robusna navigacija, manipulacija objektima te intuitivna komunikacija s korisnicima. Većina robotskih platformi podržava samo jednu ili dvije od navedenih vještina. U ovome se članku predstavlja robotska platforma Dynamaid za istraživanje uslužnih djelatnosti u kućanstvima. U svrhu robusne navigacije, Dynamaid ima bazu s četiri zasebno upravljiva diferencijalna pogona, što omogućava svesmjerno gibanje. U svrhu manipulacije, Dynamaid je dodatno opremljen s dvije antropomorfne robotske ruke s hvataljkom te tijelom koje se može zakretati i dizati. Za intuitivnu višenamjensku komunikaciju, robot sadrži mikrofon, stereo kameru te pokretnu glavu. Njegovo čovjekoliko tijelo podržava prirodnu interakciju. Robot ima sposobnost uočavanja ljudi u svom okruženju, prepoznavanja i sinteze govora. Razvijen je software za sudjelovanje na RoboCup@Home natjecanjima, koja služe kao referenta mjera za kućanske uslužne robote. Naša grupa, Nimbro@Home, sudjelovala je s Dynamaidom i našim robotom za komunikaciju, Robotinhom, na natjecanjima RoboCup German Open 2009 i RoboCup 2009, gdje smo osvojili drugo i treće mjesto. Također smo dobili nagradu za inovativan dizajn robota, empatičko ponašanje te robot-robot suradnju

Hierarchical Reactive Control for Soccer Playing Humanoid Robots

What drives thousands of researchers worldwide to devote their creativity and energy t

Heterogeneous context-aware robots providing a personalized building tour regular paper

Existing robot guides offer a tour of a building, such as a museum or science centre, to one or more visitors. Usually the tours are predefined and lack support for dynamic interactions between the different robots. This paper focuses on the distributed collaboration of multiple heterogeneous robots (receptionist, companion) guiding visitors through a building. Semantic techniques support the formal definition of tour topics, the available content on a specific topic, and the robot and person profiles including interests and acquired knowledge. The robot guides select topics depending on their participants' interests and prior knowledge. Whenever one guide moves into the proximity of another, the guides automatically exchange participants, optimizing the amount of interesting topics. Robot collaboration is realized through the development of a software module that allows a robot to transparently include behaviours performed by other robots into its own set of behaviours. The multi-robot visitor guide application is integrated into an extended distributed heterogeneous robot team, using a receptionist robot that was not originally designed to cooperate with the guides. Evaluation of the implemented algorithms presents a 90% content coverage of relevant topics for the participants

Guido and Am I Robot? A Case Study of Two Robotic Artworks Operating in Public Spaces

This article is a case study of two artworks that were commissioned for and exhibited in art venues in 2016 and 2017. The first artwork, Guido the Robot Guide, guided the visitors to an art-science exhibition, presenting the exhibits with a robot's perspective. Guido was the result of a collaboration between artists and engineers. The concept was an irreverent robot guide that could switch transparently from autonomous mode to operator control, allowing for seamless natural interaction. We examine how the project unfolded, its successes and limitations. Following on Guido, the lead artist developed the robotic installation Am I Robot? where the idea of a hybrid autonomous/remote-manual mode was implemented fully in a non-utilitarian machine that was exhibited in several art galleries. The article provides a concise contextualisation and details technical and design aspects as well as observations of visitors' interactions with the artworks. We evaluate the hybrid system's potential for creative robotics applications and identify directions for future research

F-formation Detection: Individuating Free-standing Conversational Groups in Images

Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On

International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014

Proceedings of International Summerschool Computer Science 201

Towards a framework for socially interactive robots

250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa línea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guía y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creíbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo físico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para así poder mejorar su sociabilida

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems