A bio-inspired robot with visual perception of affordances

Abstract

We present a visual robot whose associated neural controller develops a realistic perception of affordances. The controller uses known insect brain principles; particularly the time stabilized sparse code communication between the Antennal Lobe and the Mushroom Body. The robot perceives the world through a webcam and canny border openCV routines. Self-controlled neural agents process this massive raw data and produce a time stabilized sparse version, where implicit time-space information is encoded. Preprocessed information is relayed to a population of neural agents specialized in cognitive activities and trained under selfcritical isolated conditions. Isolation induces an emergent behavior which makes possible the invariant visual recognition of objects. This later capacity is assembled into cognitive strings which incorporate time-elapse learning resources activation. By using this assembled capacity during an extended learning period the robot finally achieves perception of affordances. The system has been tested in real time with real world elements.