This paper presents a way that enables robots to learn abstract concepts from sensory/perceptual data. In order to overcome the gap between the low-level sensory data cind higher-level concept description, a method called feature abstraction is used. Feature abstraction dynamically defines abstract sensors from primitive sensory devices and makes it possible to learn appropriate sensory-motor constraints. This method has been implemented on a reed mobile robot as a learning system called ACORN-II. ACORN-II was evaluated with some empirical results eind shown that the system can learn some abstract concepts more accurately than other existing systems.
