XPERIENCE - Robots Bootstrapped through Learning from Experience

author: Rüdiger Dillmann, Humanoids and Intelligence Systems Lab, Karlsruhe Institute of Technology (KIT)
published: March 14, 2012,   recorded: February 2012,   views: 2979
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Description

Current research in enactive, embodied cognition is built on two central ideas: 1) Physical interaction with and exploration of the world allows an agent to acquire and extend intrinsically grounded, cognitive representations and, 2) representations built from such interactions are much better adapted to guiding behaviour than human crafted rules or control logic. Exploration and discriminative learning, however are relatively slow processes. Humans, on the other hand, are able to rapidly create new concepts and react to unanticipated situations using their experience. “Imagining” and “internal simulation”, hence generative mechanisms which rely on prior knowledge are employed to predict the immediate future and are key in increasing bandwidth and speed of cognitive development. Current artificial cognitive systems are limited in this respect as they do not yet make efficient use of such generative mechanisms for the extension of their cognitive properties.

Solution:
The Xperience project will address this problem by structural bootstrapping, an idea taken from child language acquisition research. Structural bootstrapping is a method of building generative models, leveraging existing experience to predict unexplored action effects and to focus the hypothesis space for learning novel concepts. This developmental approach enables rapid generalization and acquisition of new knowledge and skills from little additional training data. Moreover, thanks to shared concepts, structural bootstrapping enables enactive agents to communicate effectively with each other and with humans. Structural bootstrapping can be employed at all levels of cognitive development (e.g. sensorimotor, planning, communication).

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Download slides icon Download slides: cogsys2012_dillmann_bootstrapped_01.pdf (2.0 MB)


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