In intelligent robot design

the traditional machine learning paradigm is commonly used.However

the traditional methods cause problems in visual tasks such as low learning initiative

lack of adaptability with uncertainty and bad expansibility of knowledge and ability.According to the new research direction called cognitive development learning

a visual novelty driven incremental and autonomous visual learning algorithm is proposed

in which the internal motivation is defined as visual novelty which is calculated by online PCA.The autonomous learning and accumulation of knowledge is implemented in the form of updating PCA subspace

which is guided by internally motivated Q-learning using visual novelty.Equipped with the proposed algorithm

a robot makes the next learning decision by judging the novelty between learned knowledge and what is seen now.Experimental results show that the algorithm has the ability of autonomous exploring and learning

actively guiding the robot to learn new knowledge

acquire knowledge and develop intelligence online and in incremental manner.