Making a robotic scene representation accessible to feature and label queries

We present a neural architecture for scene representation that stores semantic information about objects in the robot´s workspace. We show how this representation can be queried both through low-level features such as color and size, through feature conjunctions, as well as through symbolic labels. This is possible by binding different feature dimensions through space and integrating these space-feature representations with an object recognition system. Queries lead to the activation of a neural representation of previously seen objects, which can then be used to drive object-oriented action. The representation is continuously linked to sensory information and autonomously updates when objects are moved or removed.