FIAS Scientific Report 2011 - Frankfurt Institute for Advanced Studies ...

Developmental robotics
Collaborators: P. Chandrashekhariah 1 , G. Spina 1 , D. Krieg 1 , D. Pamplona 1 , C. Rothkopf 1 , J. Triesch 1 , B. Shi 2 ,
Z. Yu 2
1 Frankfurt Institute for Advanced Studies, 2 Hong Kong University of Science and Technology
In this line of research we investigate how principles of learning and development from biological organisms
can be exploited to build autonomously learning robots. Along these lines we have developed a “curious”
active vision system that autonomously explores its environment and learns object representations without any
human assistance. Similar to an infant, who is intrinsically motivated to seek out new information, our system
is endowed with an attention and learning mechanism designed to search for new information that has not been
learned yet. Our method can deal with dynamic changes of object appearance which are incorporated into the
object models.
In a second line of research in collaboration with researchers at the Hong Kong University of Science and Technology
we are working on methods for efficient coding of sensory information in the perception-action-cycle.
We have been developing a new way to combine unsupervised learning of generative models with reinforcement
learning and apply this to the learning of disparity tuning and vergence eye movements. Concretely, a
generative model is learning to jointly encode the left and right images. At the same time, the system is receiving
internal reward signals for generating eye movements that make the left and right images easier to encode
for the generative model. This way it learns to perform vergence eye movements that align the left and right
images making them maximally redundant. Our approach explains how a the ability to properly align the two
eyes and lean a representation of binocular disparity can develop and self-calibrate completely autonomously
on the basis of efficient coding principles.
Figure: The iCub robot head used in our studies. Its degrees of freedom and appearance are modeled after a 2-year-old
child.
Related publications in 2011:
1) P. Chandrashekhariah, Q. Wang, G. Spina, Familiarity-to-novelty shift driven by learning: a conceptual and
computational model, IEEE Int. Conference on Development and Learning and Epigenetic Robotics (ICDL-
EpiRob), 2011.
2) P. Chandrashekhariah, G. Spina, J. Triesch, Let it learn: A curious vision system for autonomous object
learning, in preparation.
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