Wspólnotowy Serwis Informacyjny Badan i Rozwoju - CORDIS


SIGNAL Streszczenie raportu

Project ID: IST-2000-29225
Źródło dofinansowania: FP5-IST
Kraj: Germany

Learning robot control based on an internal value system

Learning Robot Control based on an Internal Value System:
The underlying architectural concept behind the control structure is the
Systemic Architecture Approach developed within the SIGNAL reserach project (ist-2000-29225). This approach implements the controller by a set of modules with
distinct functionality. These modules are organised in layers, where higher layers are designed to implement higher functions. In addition, the architecture is organised into two main branches: one branch going from low level sensory data, upwards to higher sensory capabilities (sensory upstream). The other branch is directed from high level, highly sophisticated control schemes, down to low level motor functions (actuatory downstream). Although this architecture is not coping to cover all possible control schemes, a wide variety of modern controller designs can be easily realised using this approach.

The hierarchical structure of the robot control system is divided into five major functional sections:
- The robot in its environment; this can be a real robot or a simulation of both.

- The sensory upstream; with stages of sensory functions with increasing complexity.

- The actuatory downstream; with stages of decreasing action or behavioural complexity.

- An Internal Value System IVS, ("drives","emotions") based on sensory values to govern the action selection mechanism. The IVS values serve as input to the action selection and as input to the action selection learning modules.

- The action selection mechanism; activating actions, action programs and complete behaviours of different complexity. The action selection is designed to be learnable.

Getting autonomous robots to do the things we want them to do is a challenge. Even the definition of parameters for a given controller type is very hard or realise for
interesting robotic tasks. Designing controllers that are easily configured in an adequate way is far more complicated. The idea to make a controller learn an adequate set of parameters and functions for a given task is not completely new, but still not solved sufficiently.

The developed system is a widely useable approach for intelligent control of autonomous robots and autonomous agents.

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Div. of Neural Computation, University of Bonn
Roemerstr. 164
53117 Bonn
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