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中国图象图形学报 2012
Visual novelty driven incremental and autonomous visual learning algorithm
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Abstract:
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.