Connectionists: 2 Scholarships for Postdocs at CoR-Lab / Bielefeld University

[Carola Haumann]

Apologies for multiple postings

2 Scholarships for Postdocs at CoR-Lab / Bielefeld University

The CoR-Lab has been established at Bielefeld University, Germany, as a 
research centre for intelligent systems and human-machine interaction. 
The CoR-Lab forms a strategic partnership between Bielefeld University 
and the Honda Research Institute Europe GmbH, Germany. It pursues 
fundamental research in the field of cognitive robots and intelligent 
systems, where the Honda humanoid robot ASIMO is available as an 
advanced technological platform. A particular focus of the CoR-Lab is 
the interdisciplinary integration of expertise in engineering, computer 
science, brain science, and cognitive sciences, including the humanities 
and social sciences.

The Graduate School that is associated with the CoR-Lab provides an 
exciting and stimulating environment for enthusiastic and creative 
postdocs, allowing them to pursue research in international teams in 
close collaboration with an industrial research institute. The CoR-Lab 
Graduate School offers 2 scholarships for postdocs. We invite 
applications from researchers holding an academic degree (Dr./Ph.D.) and 
meeting the qualifications listed below in detail for both positions. 
Fluency in English is required.

A complete application should include certificates and transcripts of 
records of the completed course of studies, a CV, a cover letter 
providing information about the qualification and the motivation to do 
research in the Graduate School, as well as a short description of the 
research interests with regard to one of the following two projects:

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*Implicit semantic transmission in social learning Analysis and modeling

The social context of learning has increasingly gained attention in 
developmental psychology, cognitive science and robotics.  It has been 
proposed that an agent – in order to learn – needs to be grounded in a 
meaningful embodied activity. The robotic research has just started to 
benefit from the use of developmental approaches: Orienting towards 
‘learning by communicating’ offers new learning paradigms, within which 
it can be analyzed how semantic information is transmitted, and which 
effect the way of transmission has onto learning. So far this paradigm 
involves face-to-face scenarios, where a tutor is focusing on a student. 
However, this learning situation is not offered in every culture. 
Instead, developmental research has shown that children are likely to 
benefit also from other scenarios. Motivated by animal studies by e.g. 
Irene Pepperberg on grey parrots which were trained in a social learning 
paradigm (model-rival-paradigm), it is our goal to investigate 
multi-party learning scenarios, in which the tutor does not address the 
student directly but the student is learning while observing a tutoring 
behaviour towards another person. Thus, our assumption is that learning 
can take place from both, direct and indirect teaching.

With this project, we will investigate the behaviour of tutors and 
students and study the achieved learning effects in different situations 
of social learning. Based on the data gathered in psychophysical 
experiments on both, direct and indirect teaching scenarios, we aim to 
identify different verbal and non-verbal patterns, e.g. denominating 
objects, showing an object. Following the identification and 
classification of these patterns, we aim to develop a generative model 
for their production. The purpose of this model is twofold. Firstly, it 
will allow setting up a virtual tutor. A virtual tutor can be used to 
create simulated dialogues with the virtual tutor replacing the real 
tutor or tutors and an additional avatar, which replaces the child. 
Secondly, building a generative model for the behaviour of the tutor 
will allow us to understand the underlying principles of learning in a 
social context better and the insights from the modelling will provide 
valuable feedback on the design of the psychophysical experiments.

The results of this research should enable the setup of a social 
interaction simulation environment, where reproducible experiments 
between tutor avatars and a robotic artefact could be performed. These 
experiments will allow testing new hypotheses on how social learning 
takes place.

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* Autonomous Exploration of Manual Interaction Space

We gradually increase our manual competence by exploring manual 
interaction spaces for many different kinds of objects. This is an 
active process that is very different from passive perception of 
"samples". The availability of humanoid robot hands offers the 
opportunity to investigate different strategies for such active 
exploration in realistic settings. In the present project, the 
investigation of such strategies shall be pursued from the perspective 
of „multimodal proprioception:“ correlating joint angles, partial 
contact information from touch sensors and joint torques as well as 
visual information about changes in finger and object position in such a 
way as to make predictions about "useful aspects" for shaping the 
ongoing interaction.
To make this very ambitious goal approachable within the resource bounds 
of a single project, we will focus on an interesting and important 
specific case of manual interaction spaces: „visually supervised 
object-in-hand manipulation“. More particularly, one could consider 
rotating an object, e.g. a cube, within the hand such, that certain 
faces become visible one after the other.
This project crucially involves the need to combine visual information 
with proprioceptive feedback when the fingers explore the faces and 
edges of the object. A major goal of the project would be to implement a 
"vertical slice" of explorative skills, ranging from low level finger 
control and visual perception within an object category, chunking a 
limited set of action primitives, and planning short action sequences.
Generic insights should be about how visual and haptic information has 
to be combined to drive the exploration process and about suitable 
principles for shaping the exploration, such as reinforcement learning, 
active learning driven by information maximization, imitation of 
previously learnt episodes (instead of statistical learning).

Research experience in one or more of the areas visual perception, 
robotics control, reinforcement learning, active learning, and neural 
networks is appreciated.

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For more information please see:
http://www.cor-lab.de/corlab/html/graduate_school/index.php

Please send your application until 13 May 2008 (preferably in PDF 
format) to the Managing Director of the Graduate School:

email: chaumann at cor-lab.uni-bielefeld.de

Bielefeld University
CoR-Lab Graduate School
Dr. Carola Haumann
33594 Bielefeld
Germany