Cosy Meeting of Minds Workshop
List of Posters to be presented by members of the CoSy
Hendrik Zender, Patric Jensfelt, Oscar Martinez Mozos, Geert-Jan M. Kruijff, and Wolfram Burgard (2007)
A major challenge in robotics and AI lies in creating robots that are to
cooperate with people in human-populated environments, e.g. for domestic
assistance or elderly care. Such robots need skills that allow them to interact
with the world and the humans living and working there. In this work we
investigate the question of spatial understanding of human-made environments.
The functionalities of our system comprise perception of the world, natural
language, learning, and reasoning. For this purpose we integrate
state-of-the-art components from different disciplines in AI, robotics, and
cognitive systems into a mobile robot system.
Here we describe the principles that were used for the integration, including
cross-modal ontology- based mediation, and processing of perception on multiple
levels of abstraction. Finally, we present experiments with the integrated "CoSy
Explorer" system and list some major lessons that were learned from its design,
implementation, and evaluation.
Danijel Skocaj, Alen Vrecko, Matej Kristan, Barry Ridge, Gregor Berginc, and Ales Leonardis
"A System for Continuous Learning of Visual
We present an artificial system for learning simple visual concepts. It
comprises of vision, communication and manipulation sub- systems, which provide
visual input, enable verbal and non-verbal communication with a tutor and allow
interaction with a given scene. The main goal is to learn associations between
automatically extracted visual features and words that describe the scene in an
open-ended, continuous manner. In particular, we address the problem of
cross-modal learning of visual properties and spatial relations. We introduce
and analyse several learning modes requiring different levels of tutor
Sanja Fidler and Ales Leonardis
"Towards Scalable Representations of Object
Categories: Learning a Hierarchy of Parts"
We propose a novel approach to constructing a hierarchical
representation of visual input that aims to enable recognition and detection of
a large number of object categories. Inspired by the principles of efficient
indexing, robust matching, and ideas of compositionality, our approach learns a
hierarchy of spatially flexible compositions, i.e. parts, in an unsupervised,
statistics-driven manner. Starting with simple, frequent features, we learn the
statistically most significant compositions (parts composed of parts), which
consequently define the next layer. Parts are learned sequentially, layer after
layer, optimally adjusting to the visual data. The lower layers are learned in a
category-independent way to obtain complex, yet sharable visual building blocks,
which is a crucial step towards a scalable representation. Higher layers of the
hierarchy, on the other hand, are constructed by using specific categories,
achieving a category representation with a small number of highly generalizable
parts that gained their structural flexibility through composition within the
hierarchy. Built in this way, new categories can be efficiently and continuously
added to the system by adding a small number of parts only in the higher layers.
The approach is demonstrated on a large collection of images and a variety of
object categories. Detection results confirm the effectiveness and robustness of
the learned parts.
Nick Hawes, Aaron Sloman, Jeremy Wyatt, Henrik Jacobsson, Geert-Jan M. Kruijff, Michael Brenner, Gregor Berginc and Danijel Skocaj
We present integration mechanisms for combining heterogeneous
components in a situated information processing system, illustrated by a
cognitive robot able to collaborate with a human and display some understanding
of its surroundings. These mechanisms include an architectural schema that
encourages parallel and incremental information processing, and a method for
binding information from distinct representations that when faced with rapid
change in the world can maintain a coherent, though distributed, view of it.
Provisional results are demonstrated in a robot combining vision, manipulation,
language, planning and reasoning capabilities interacting with a human and
Henrik Jacobsson, Geert-Jan Kruijff and Maria Staudte
"Language Acquisition from Neural and
A fundamental learning problem in adaptive, embodied cognitive
systems is how to learn discrete models of situated experience which can mediate
between sensorimotoric expe- rience and high-level cognitive processes (such as
language and planning). Recent approaches include learning of discrete Hidden
Markov Models for behaviour description and active (curiosity driven) learning
of perception prediction. Through curiosity driven learning, also communication
itself can be "discovered" among other actions as a source of rich information.
It can also be argued that in order to learn a situated form of language that
takes into account the embodiment of the robot, there needs to be a close
correspon- dence between the sensorimotor and linguistic capabilities. The robot
should communicate about what it knows, and through active learning it could
know how its actions will affect the environment.
Michael Brenner, Nick Hawes, John Kelleher and Jeremy Wyatt
Poster presented at IJCAI 2007:
"Mediating Between Qualitative and
Quantitative Representations for Task-Orientated Human-Robot Interaction"
A robot can only display long-term intelligent behaviour, in
particular when interacting with humans, if it can reason qualitatively about
current and future states of the world. On the other hand, being an embodied
cognitive system a robot must also interact with the real world and therefore
must link its qualitative representations to perceptions and actions in
continuous space. For this kind of mediation between representations our paper
A generic method for using formal planning operators to guide
the interpretation of natural language commands and automatically translate
to formal planning goals.
A novel method for matching spatial models to continuous
scenes and interpreting qualitative referential expressions.
Somboon Hongeng and ...
Something on action perception
Unsupervised Body Scheme Learning
In this paper, we present an approach allowing a robot to
learn a generative model of its own physical body from scratch using
self-perception with a single monocular camera. Our approach yields a compact
Bayesian network for the robot's kinematic structure including the forward and
inverse models relating action commands and body pose. We propose to
simultaneously learn local action models for all pairs of perceivable body parts
from data generated through random "motor babbling." From this repertoire of
local models, we construct a Bayesian network for the full system using the pose
prediction accuracy on a separate cross validation data set as the criterion for
model selection. The resulting model can be used to predict the body pose when
no perception is available and allows for gradient-based posture control. In
experiments with real and simulated manipulator arms, we show that our system is
able to quickly learn compact and accurate models and to robustly deal with
Aaron Sloman [Did not find time to do this
A poster on the problem of identifying requirements for
a human-like robot, and some unobvious requirements.
Identifying what humans, at various stages of development, and for that
matter other animals, can and cannot do is not an easy task. The obvious means,
namely observing their actions, is fraught with problems because it is
impossible to tell from any observed behaviour what competence underlies
that behaviour. Paradoxically, it is possible to gain insight into possible
explanations of the competence of a human or other animal by attempting to
design robots capable of performing a similar variety of tasks provided that one
studies a broad enough variety in enough detail, since a working artificial
model that appears to explain some performance may fail to "scale out" to
produce other performances in other contexts as natural competences do. Some of
the requirements for performing those tasks become evident only as a result of
developing and testing designs and investigating reasons why they do not work.
It is also possible to do this retrospectively for the field as a whole: namely
developing requirements by analysing limitations of work done so far and trying
to understand what is needed for further progress. Theoretical work of that sort
has played a significant role in the CoSy project, and some of the results in
the first two years are in the papers on 'Requirements' and 'Architectures' in
Work Package 1:
Architecture and Representation. Other relevant work includes the
roadmap project and
proposal for de-fragmenting AI.
Last updated 3 Oct 2007