Tijn van der Zant

RoboCup X: a proposal for a new league where robocup goes real world

To put more emphasis on real-world problems, the authors propose to extend the RoboCup competitions. In order to foster progress in the desired abilities the authors propose to expand the existing challenges by a set of simple tests. The passing of the entire set should lead to robots that are capable of working both autonomously and in cooperation with humans in different realistic scenarios. Robots from all RoboCup leagues but also from outside of RoboCup should be allowed to participate. The new league especially aims at fostering the development of practical solutions and applications for supporting humans in everyday life.

RoboCup@Home: adaptive benchmarking of robot bodies and minds

RoboCup@Home is the largest benchmarking effort for domestic service robots. The benchmarking is in the form of a competition, with several yearly local competitions and an international one. Every year the tests become more complex, depending on the results of the previous years. In the past four years the focus has been on benchmarking physical aspects of the robots, such as manipulation, recognizing people and human-robot interaction. In 2010, for the first time, there is a test which is targeted at the mental cognitive capabilities of the robot. In order to guarantee scientific quality of the proposed solutions and effective integration in a fully working system, all the tests include different capabilities and change every year. This novel feature of RoboCup@Home benchmarking raises the question of: How can effective benchmark tests be defined and at the same time measure the progress over many years? In this paper we present the methodology applied in and results of RoboCup@Home for measuring the effectiveness of benchmarking service robots through competitions and present a new integrated test for benchmarking the cognitive abilities of a robot.

RoboCup@Home

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences.One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions.In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition.We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics.

Domestic Service Robots in the Real World

The use of robots in domestic environments has increased largely in the recent years. There are already several millions robots used for basic household chores (e.g. vacuum cleaning) or for entertainment. It is expected that domestic service robots become more and more useful, autonomous, multipurpose and reliable, and that they will be able to interact with humans and objects in the real world in a natural way. This challenge poses a large number of unsolved problems across many scientific disciplines. The aim of this special issue is to present recent advances in domestic service robotics, showing robots that are able to operate in real home

Finding good Echo State Networks to control an underwater robot using evolutionary computations

Abstract The control of an underwater robot is difficult due to non-linear effects. Echo State Networks (ESNs) provide a way of dealing with non-linearity. The quality of an Echo State Network (ESN) (H. Jaeger, 2001, 2002) depends strongly on its topology. Usually an educated guess combined with a brute force method is used to obtain an ESN that, after training, produces a low error. In this article the authors suggest another way to find good topologies by using evolution. Two heuristics, evolutionary algorithms and evolutionary strategies, are compared. The proposed method outperforms standard ones like ARX and backpropagation networks on the data from the Twin-Burger underwater robot.

Bio-sensing for Emotional Characterization without Word Labels

In this article, we address some of the issues concerning emotion recognition from processing physiological signals captured by bio-sensors. We discuss some of our preliminary results, and propose future directions for emotion recognition based on our lessons learned.

Recognition of Handwritten Numerical Fields in a Large Single-Writer Historical Collection

This paper presents a segmentation-based handwriting recognizer and the performance that it achieves on the numerical fields extracted from a large single-writer historical collection. Our recognizer has the particularity that it uses morphing during training: random elastic deformations are applied to fabricate synthetic training character patterns yielding an improved final recognition performance. Two different digit recognizers are evaluated, a multilayer perceptron (MLP) and radial basis function network (RBF), by plugging them into the same left-to-right Viterbi search framework with a tree organization of there cognition lexicon. We also compare with the performance obtained when no dictionary is used to constrain the recognition results.

Where are the Search Engines for Handwritten Documents

Abstract Although the problems of optical character recognition for contemporary printed text have been resolved, for historical printed and handwritten connected cursive text (i.e. western style writing), they have not. This does not mean that scanning historical documents is not useful. This article describes our research on retrieving digitized handwritten documents containing the information that the user is looking for. This task is essential for optimizing the archives work. We investigated how to process historical documents and their transcriptions, so that a super computer could learn how to read. We applied artificial intelligence techniques to a large amount of image data and created a search engine. Our methods often require that the computer learns in interaction with a human. We have studied the requests of archive users in order to bring our research as close as possible to the current information needs. Our system learns continuously, allowing the constant improvement of search results. User requests stimulated us to delve into an unsolved topic: to search for the most elusive knowledge in text, namely the names of people and places. The solutions are described in this article.

Large scale parallel document image processing

Building a system which allows to search a very large database of document images requires professionalization of hardware and software, e-science and web access. In astrophysics there is ample experience dealing with large data sets due to an increasing number of measurement instruments. The problem of digitization of historical documents of the Dutch cultural heritage is a similar problem. This paper discusses the use of a system developed at the Kapteyn Institute of Astrophysics for the processing of large data sets, applied to the problem of creating a very large searchable archive of connected cursive handwritten texts. The system is adapted to the specific needs of processing document images. It shows that interdisciplinary collaboration can be beneficial in the context of machine learning, data processing and professionalization of image processing and retrieval systems.

Generative Artificial Intelligence

The closed systems of contemporary Artificial Intelligence do not seem to lead to intelligent machines in the near future. What is needed are open-ended systems with non-linear properties in order to create interesting properties for the scaffolding of an artificial mind. Using post-structuralistic theories of possibility spaces combined with neo-cybernetic mechanisms such as feedback allows to actively manipulate the phase space of possibilities. This is the field of Generative Artificial Intelligence and it is implementing mechanisms and setting up experiments with the goal of the creation of open-ended systems. It sidesteps the traditional argumentation of top-down versus bottom-up by using both mechanisms. Bottom-up procedures are used to generate possibility spaces and top-down methods sort out the structures that are functioning the worst. Top-down mechanisms can be the environment, but also humans who steer the development processes.