In this paper, we present our on-going research to allow humanoid robots to learn spatial perception. We are using artificial neural networks (ANN) to estimate the location of objects in the robot’s environment. The method is using only the visual inputs and the joint encoder readings, no camera

: spatial understanding, object localisation, humanoid robot, neural network, genetic programming

We use a Katana robotic arm to teach an iCub humanoid robot how to perceive the location of the objects it sees. To do this, the Katana positions an object within the shared workspace, and tells the iCub where it has placed it. While the iCub moves it observes the object, and a neural network

I hereby declare that I have created this work completely on my own and used no other sources or tools than the ones listed, and that I have marked any citations accordingly. Hiermit versichere ich, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel benutzt sowie Zitate kenntlich gemacht habe.

completely event-based mode, supporting the execution of the game in an discrete-event-based network simulator. Third, a simple artificial intelligence player is developed for workload generation in large (and possibly simulated) networks. Furthermore, the newly developed transport protocol CUSP is applied

in partial ful2llment of the requirements for the degree of

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility. At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources. This article pre-sents such a bioinformatics curric-ulum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field. Online Learning Comes of Age Online academic ‘‘courseware’ ’ at the university level has now been available to the public for a decade, the earliest concerted effort having originated in 2002 with the Massachusetts Institute of Technology (MIT) and their OpenCour-seWare initiative