TY - GEN
T1 - Autonomous learning of robust visual object detection and identification on a humanoid
AU - Leitner, Jürgen
AU - Chandrashekhariah, Pramod
AU - Harding, Simon
AU - Frank, Mikhail
AU - Spina, Gabriele
AU - Förster, Alexander
AU - Triesch, Jochen
AU - Schmidhuber, Jürgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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. © 2012 IEEE.
AB - 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. © 2012 IEEE.
UR - http://ieeexplore.ieee.org/document/6400826/
UR - http://www.scopus.com/inward/record.url?scp=84872861649&partnerID=8YFLogxK
U2 - 10.1109/DevLrn.2012.6400826
DO - 10.1109/DevLrn.2012.6400826
M3 - Conference contribution
SN - 9781467349635
BT - 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012
ER -