A behavior-based mobile robot architecture for Learning from Demonstration
Autonomous mobile robots (AMRs), 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, behavior-based 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.
online learning from demonstration behavior-based architecture behavior classification growing neural net mobile service robot