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Dynamic Decision Making for Humanoid Robots Based on a Modular Task Structure

Within the last years a new challenging field of robotics has arisen in which assisting robots cooperate and interact with humans, both imitating their behaviour patterns and performing with them and/or in their environment diverse complex tasks. Due to the ambitious tasks and the complexity of a time varying environment, these ''humanoid'' robots have to be provided with much more intelligence in comparison with the current industrial robots available on the market. Thus, the robot has to dispose on the one hand of a large variety of dedicated sensors which cover the full range of human perception modalities and which collect information about the state of the robot and of the environment. On the other hand the control concept has to provide the capability of extracting, combining and analyzing this information in order to cope autonomously with these extreme conditions in the most appropriate way. Moreover, in order to be able to face a manifold of different possible situations, the robot has to manage specific human-like skills. That means it has to perceive, act, deliberate and communicate in a robust manner.