Over the last two decades there has been a increase in sophistication in research and edutainment
robots. From Honda’s
Asimo series to
Aldebaran's Nao, all are amazing feats of mechanical and electrical
engineering. These robots can navigate around the house, mow the lawn and even walk. However, no robot
recognizes its owner, nor is it able to read moods, or strike up a basic conversation or learn about its
environment: current state-of-art robots lack cognitive and social capabilities. At the Centre for Interactive Intelligent Systems we
investigate how robots could learn to imitate humans or even other robots, how they can learn about new
concepts and how they can learn to communicate with humans.
We are studying the frontiers of cognitive and developmental robotics in the
EU FP7 ITALK and
CONCEPT projects.
Humans are a symbolic species,
meaning that they cut up their perception of the
world into manageable chunks. We are specifically
interested in how humans handle this chunking of the
very basic senses. I investigate the nature of
perceptual categories, and in particular, the nature
of colour categories. Colour categories exhibit some
almost universal properties; for example, almost
every culture has a word like "red", designating
that almost everyone possesses a category for red;
but few people have a word and category for
"turquoise". There are several hypotheses why this
is so. Some claim colour categories are in some way
innate, others believe colour categories are learnt
through interacting with the environment, while
others believe that culture plays a role in shaping
colour categories. I use artificial intelligence
techniques to test drive these hypotheses, and the
results lead me to believe that culture is the main
catalyst for shaping colour categories (long
abstract and PhD
thesis).
Using genetic programming we evolved
task-specific visual feature detectors, which were
then used as the first input stage for visual
discrimination algorithms. What basic functionality
should be present? How can we generate this basic
and more complex functionality? What kind of
computational methods (genetic programming, genetic
algorithms, or other optimisation techniques) can we
use? How can evolutionary and self-organizational
principles be applied to the generation and
selection of visual feature detectors?
I have built and programmed several autonomous robots. My specific interest is
in behaviour-based robots, robots that exhibit
life-like behaviour with only a minimal amount of
computation. I had a special interest in
heterogeneous groups of autonomous systems, where we
studied how robots with different bodies can
cooperate and coexist in a shared environment.I joined the AI Lab Robocup98 and RoboCup99 teams, headed by Andreas Birk. I constructed the vision system for our five-robot soccer team. The vision accurately tracked and predicted the position of ten robots (our team and the opponents) and a ball in real-time.
In the Talking Heads experiment is a large-scale demonstration of theories on the evolution of language. In the experiment robots observe the world and construct a language to describe the world to each other. The experiments demonstrated concepts on lexicon formation, meaning creation, and the self-organisation of language.