Design and simulation of vehicle controllers through genetic algorithms

Genetic Programming (GP) is a population-based evolutionary technique, which, unlike a Genetic Algorithm (GA) does not work on a fixed-length data structure, but on a variable-length structure and aims to evolve functions, models or programs, rather than finding a set of parameters. There are different histories of driver development, so different proposals of the use of PG to evolve driver structures are presented. In the case of an autonomous vehicle, the development of a steering controller is complex in the sense that it is a non-linear system, and the control actions are very limited by the maximum angle allowed by the steering wheels. This paper presents the development of an autonomous vehicle controller with Ackermann steering evolved by means of Genetic Programming

Rising Tide 2016

Research and scholarship highlights from University of New England community members. This issue highlights student and faculty research and projects within UNE\u27s College of Arts and Sciences, College of Dental Medicine, College of Osteopathic Medicine, College of Pharmacy, Westbrook College of Health Professions, and projects and research from UNE\u27s Centers for Excellence.https://dune.une.edu/risingtide/1005/thumbnail.jp

Interactive Imitation Learning in Robotics: A Survey

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Uncanniliy Human - Experimental Investigation of the Uncanny Valley Phenomenon

Seit seiner Einführung in den wissenschaftlichen Diskurs im Jahr 1970 (Mori, 1970; Mori et al., 2012) ist das Uncanny Valley eine der meist diskutierten und referenzierten Theorien in der Robotik. Obwohl die Theorie vor mehr als 40 Jahren postuliert wurde, wurde sie kaum empirisch untersucht. Erst in den letzten sieben Jahren haben Wissenschaftler aus dem Bereich Robotik, aber auch aus anderen Disziplinen, angefangen, das Uncanny Valley systematischer zu erforschen. Allerdings blieben bisher viele Fragen offen. Einiger dieser Fragen wurden in dem vorliegenden Forschungsprojekt im Rahmen von vier aufeinander aufbauenden Studien untersucht. Der Schwerpunkt der Arbeit liegt auf der systematischen Untersuchung des Einflusses von statischen und dynamischen Merkmalen von Robotern, wie etwa dem Design bzw. Erscheinungsbild und der Bewegung, auf die Wahrnehmung und Evaluation von diesen Robotern. Eine Besonderheit der vorliegenden Arbeit ist der multi-methodologische Ansatz, bei dem die durch verschiedenste Methoden und Messinstrumente beobachteten Effekte auf ihre Relevanz für die Uncanny Valley Theorie hin untersucht wurden. Zudem wurden die in der bisherigen Literatur postulierten Erklärungsansätze für den Uncanny Valley Effekt empirisch getestet. In der ersten Studie wurde anhand von qualitativen Interviews, in denen Probanden Bilder und Videos von humanoiden und androiden Robotern gezeigt wurden, untersucht, wie Probanden sehr menschenähnliche Roboter evaluieren, ob sie emotionale Reaktionen zeigen, und wie ihre Einstellungen gegenüber diesen Robotern sind. Die Ergebnisse zeigen, dass emotionale Reaktion, wenn überhaupt vorhanden, individuell sehr verschieden ausfallen. Das Erscheinungsbild der Roboter war sehr wichtig, denn bestimmte Designmerkmale wurden mit bestimmten Fähigkeiten gleichgesetzt. Ein menschliches Erscheinungsbild ohne Funktionalität wurde eher negativ bewertet. Zudem schienen die Probanden bei androiden Robotern dieselben Maßstäbe zur Bewertung von Attraktivität anzulegen wie sie dies bei Menschen tun. Die Analyse zeigte auch die Relevanz der Bewegungen der Roboter und des Kontextes, in welchem der jeweilige Roboter präsentiert wurde. Es wurde erste Evidenz gefunden für die Annahme, dass Menschen Unsicherheit verspüren bei der Kategorisierung von androiden Robotern als entweder Roboter oder Mensch. Zudem fühlten sich die Probanden unwohl bei dem Gedanken, dass Roboter sie ersetzten könnten. Die zweite Studie untersuchte den Einfluss von robotischer Bewegung. In einem quasi-experimentellen Feldexperiment wurden Passanten mit dem androiden Roboter Geminoid HI-1 konfrontiert, der sich entweder still verhielt oder Bewegungsverhalten zeigte. Die Interaktionen wurden analysiert hinsichtlich des nonverbalen Verhaltens der Passanten (z.B. auf den Roboter gerichtete Aufmerksamkeit, interpersonale Distanz zum Roboter). Die Resultate zeigen, dass das Verhalten der Passanten von dem Verhalten des Roboters beeinflusst wurde, zum Beispiel waren die Interaktionen länger, die Probanden stellten mehr Blickkontakt her und testeten die Fähigkeiten des Roboters wenn dieser Bewegungsverhalten zeigte. Zudem diente das Verhalten des Roboters als Hinweisreiz für die richtige Kategorisierung des Roboters als solchen. Der Aspekt des Erscheinungsbildes wurde in der dritten Studie systematisch untersucht. Zu diesem Zweck wurden in einem webbasierten Fragebogen 40 standardisierte Bilder von Robotern evaluiert, um die Evaluation beeinflussende Designmerkmale zu identifizieren. Eine Clusteranalyse ergab sechs Cluster von Robotern, die auf sechs Dimensionen unterschiedlich bewertet wurden. Mögliche Beziehungen zwischen Designmerkmalen und Evaluationen der Cluster wurden aufgezeigt und diskutiert. Zudem wurde die Aussagekraft des Uncanny Valley Graphen untersucht. Ausgehend von Mori’s Überlegungen ist der Uncanny Valley Effekt eine kubische Funktion. Demnach müssten sich die Daten am besten durch eine kubische Funktion erklären lassen. Die Ergebnisse zeigten allerdings eine bessere Modellpassung für lineare oder quadratische Zusammenhänge. In der letzten Studie wurden perzeptions-orientiert und evolutionsbiologische Erklärungsansätze für das Uncanny Valley systematisch getestet. In dieser Studie wurden Daten aus Selbstauskunft, Verhaltensdaten und funktionelle Bildgebung kombiniert, um zu untersuchen ob sich die Effekte auf Basis der Selbstauskunft und der Verhaltensdaten erklären lassen durch a) zusätzliche Verarbeitungsleistung während der Perzeption von Gesichtern, b) automatisch ablaufende Prozesse sozialer Kognition, oder c) eine Überempfindlichkeit des sogenannten Verhaltensimmunsystems (behavioral immune system). Die Ergebnisse unterstützen die perzeptions-orientierten Erklärungen für den Uncanny Valley Effekt. Zum einen scheinen die Verhaltenseffekte durch neuronale Prozesse während der Wahrnehmung von Gesichtern begründet zu sein. Zum anderen gibt es Befunde, die auf eine kategoriale Wahrnehmung von Robotern und Menschen hinweisen. Evolutionsbiologische Erklärungen konnten durch die vorliegenden Daten nicht gestützt werden.Since its introduction into scientific discourse in 1970 (Mori, 1970; Mori et al., 2012) the uncanny valley has been a highly discussed and referenced theory in the field of robotics. Although the theory was postulated more than 40 years ago, it has barely been tested empirically. However, in the last seven years robot scientists addressed themselves to the task of investigating the uncanny valley more systematically. But there are still open questions, some of which have been addressed within this research in the course of four consecutive studies. This project focussed on the systematic investigation of how static and dynamic characteristics of robots such as appearance and movement determine evaluations of and behavior towards robots. The work applied a multi-methodological approach and the various observed effects were examined with regard to their importance for the assumed uncanny valley. In addition, previously proposed explanations for the uncanny valley effect were tested. The first study utilized qualitative interviews in which participants were presented with pictures and videos of humanoid and android robots to explore participants’ evaluations of very human-like robots, their attitudes about these robots, and their emotional reactions towards these robots. Results showed that emotional experiences, if existent, were very individual. The robots’ appearance was of great importance for the participants, because certain characteristics were equalized with certain abilities, merely human appearance without a connected functionality was not appreciated, and human rules of attractiveness were applied to the android robots. The analysis also demonstrated the importance of the robots’ movements and the social context they were placed in. First evidence was found supporting the assumption that participants experienced uncertainty how to categorize android robots (as human or machine) and that they felt uncomfortable at the thought to be replaced by robots. The influence of movement, as one of the important factors in the uncanny valley hypothesis, was examined in the second study. In a quasi-experimental observational field study people were confronted with the android robot Geminoid HI-1 either moving or not moving. These interactions between humans and the android robot were analyzed with regard to the participants’ nonverbal behavior (e.g. attention paid to the robot, proximity). Results show that participants’ behavior towards the android robot was influenced by the behavior the robot displayed. For instance, when the robot established eye-contact participants engaged in longer interactions, also established more eye-contact and tried to test the robots’ capabilities. The robot’s behavior served as cue for the participants to categorize the robot as such. The aspect of robot appearances was examined systematically in the third study in order to identify certain robot attractiveness indices or design characteristics which determine how people perceive robots. A web-based survey was conducted with standardized pictures of 40 different mechanoid, humanoid and android robots. A cluster analysis revealed six clusters of robots which were rated significantly different on six dimensions. Possible relationships of design characteristics and the evaluation of robots have been outlined. Moreover, it has been tested whether the data of this study can best be explained by a cubic funtion as would be suggested by the graph proposed by Mori. Results revealed that the data can be best explained by linear or quadratic relationships. The last study systematically tested perception-oriented and evolutionary-biological approaches for the uncanny valley. In this multi-methodological study, self-report and behavioral data were combined with functional magnetic resonance imaging techniques in order to examine whether the observed effects in self-report and behavior occur due to a) additional processing during face perception of human and robotic stimuli, b) automatically elicited processes of social cognition, or c) oversensitivity of the behavioral immune system. The study found strong support for perception-oriented explanations for the uncanny valley effect. First, effects seem to be driven by face perception processes. Further, there were indicators for the assumption that categorical perception takes place. In the contrary, evolutionary-biological driven explanations assuming that uncanny valley related reactions are due to oversensitivity of the behavioral immune system were not supported by this work. Altogether, this dissertation explored the importance of characteristics of robots which are relevant for the uncanny valley hypothesis. Uncanny valley related responses were examined using a variety of measures, for instance, self-reporting, behavior, and brain activation, allowing conclusions with regard to the influence of the choice of measurements on the detection of uncanny valley related responses. Most importantly, explanations for the uncanny valley were tested systematically and support was found for cognitive-oriented and perception-oriented explanations

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Third International Conference on Technologies for Music Notation and Representation TENOR 2017

The third International Conference on Technologies for Music Notation and Representation seeks to focus on a set of specific research issues associated with Music Notation that were elaborated at the first two editions of TENOR in Paris and Cambridge. The theme of the conference is vocal music, whereas the pre-conference workshops focus on innovative technological approaches to music notation