A behavior-based mobile robot architecture for learning from demonstration

Autonomous Mobile Robots (AMR), to be truly flexible, should be equipped with learning capabilities, which allow them to adapt effectively to a dynamic and changing environment. This paper proposes a modular, behaviorbased control architecture, which is particularly suited for "Learning from Demonstration"-experiments in the spatial domain. The robot learns sensory-motor-behaviors online by observing the actions of a person, another robot or another behavior. Offline learning phases are not necessary but might be used to trim the attained representation. First results applying RBF-approximation, growing neural cell structures and probabilistic models for progress estimation are presented