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.

This paper presents a method, generalization to interval, that can encode images into symbolic expressions. This method generalizes over instances of spatial patterns, and outputs a constraint program that can be used declaratively as a learned concept about spatial patterns, and procedural as a method for reasoning about spatial relations. Thus our method transforms numeric spatial patterns to symbolic declarative/procedural representations. We have implemented generalization to interval with Acorn,^ a system that acquires knowledge about spatial relations by observing 2-D raster images. We have applied this system to some layout problems to demonstrate the ability of the system and the flexibility of constraint programs for knowledge representation.