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The Cognitive Robotics Workshop is a prestigious international
workshop that takes place every two years. It is normally co-located
with one of the main AI conferences or events. Although not a rule, we
like to switch the venue between North America and Europe. The
previous workshops have been:
Research in robotics has
traditionally emphasized low-level sensing and control tasks including
sensory processing, path planning, and manipulator design and control.
In contrast, research in cognitive robotics is concerned with endowing
robots and software agents with higher level cognitive functions that
enable them to reason, act and perceive in changing, incompletely
known, and unpredictable environments. Such robots must, for example,
be able to reason about goals, actions, when to perceive and what to
look for, the cognitive states of other agents, time, collaborative
task execution, etc. In short, cognitive robotics is concerned with
integrating reasoning, perception and action with a uniform
theoretical and implementation framework.
The use of both software robots (softbots) and robotic artifacts in
everyday life is on the upswing and we are seeing increasingly more
examples of their use in society with commercial products around the
corner and some already on the market. As interaction with humans
increases, so does the demand for sophisticated robotic capabilities
associated with deliberation and high-level cognitive functions.
Combining results from the traditional robotics discipline with those
from AI and cognitive science has and will continue to be central to
research in cognitive robotics.
This workshop aims at bringing together researchers involved in all
aspects of the theory and implementation of cognitive robots, and
discussing current work and future directions. While all aspects of
cognitive robotics are of interest to the workshop, we especially
welcome discussions and demonstrations of implemented systems.
Why the workshop is of current interest
We are facing a rapidly increasing number of computer systems that are
embedded in physical devices, equipped with sensors such as web cams
and RFID tag readers, have access to smart sensor networks, are
combined through communication networks such as wireless LAN but also
through semantic grids of computation modules, and they are connected
to the World Wide Web, today's most comprehensive information source.
The degrees of freedom of a single state-of-the-art dexterous hand
exceeds those of many autonomous mobile robots. The capability of
sensing devices, the computational infrastructure, and the advent of
new actuator mechanisms suggest the realizability of unprecedented
intelligent and physically realized application systems.
However, main bottlenecks and obstacles in realizing them are in
perception, knowledge extraction from sensor data streams, action
selection, system complexity. This increase of system complexity also
causes the cost of system development and maintenance to explode, the
required lifespan of the systems to be longer, the systems to become
more fragile, and the interaction with the systems to get harder.
These problems cannot be coped with by merely improving the software
engineering techniques. Cognition in technical systems, such as
autonomous service robots, intelligent sensor-equipped environments,
and peer-to-peer software office assistants, is currently an important
focus of research in intelligent systems.
The current interest and the urge for research results is, among other
things, demonstrated by a number of national and international
research initiatives including the Darpa cognitive systems initiative
of the Information Processing Technoloy Office, various integrated
projects funded by the European Community, the British Foresight
program for cognitive systems, huge japanese research efforts, for
example in the context of Riken, to name only a few.
Another reason for the current standing of the field is that in recent
years scientific disciplines studying cognitive systems have
crossfertilized each other in various ways. Cognitive psychologists
have found convincing empirical evidence for the use of Bayes
estimation and cost function enabled control mechanisms in natural
motion control. Bayesian networks and the associated reasoning and
learning mechanisms have inspired research in cognitive psychology, in
particular the formation of causal theory in young children. Also
functional MRI images of rat brains localizing in mazes have shown
neural activation patterns of place cells similar to multimodal
probability distributions in robot localization using Bayesian
filters.
These examples suggest that (1)~successful computational mechanisms in
artificial cognitive systems tend to have counterparts with similar
functionality in natural cognitive systems; and (2)~new consolidated
findings about the structure and functional organization of perception
and motion control in natural cognitive systems indicate much better
ways of organizing and specifying computational tasks in artificial
cognitive systems.
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