Work

Autonomous learning of robust visual object detection and identification on a humanoid

Year: 2012

Type: conference-paper

Abstract: In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for training. These samples are learned for further object identification using Cartesian Genetic Programming (CGP). The learned identification is able to provide robust and fast segmentation of the objects, without using features. We showcase our system and its performance on the iCub humanoid robot.

Source: IEEE International Conference on Development and Learning (ICDL)

DOI: 10.1109/devlrn.2012.6400826