Visual Novelty based Internally Motivated Q-learning for Mobile Robot Scene Learning and Recognition
In the intelligent robot research field, the traditional machine learning paradigm is commonly used, which causes problems of 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, an incremental and autonomous visual learning algorithm based on internally motivated Q-learning is proposed for mobile robot scene learning and recognition. The visual novelty is calculated by online PCA and is considered as the internal motivation for the Q-learning robot. The active learning and accumulation of knowledge is implemented in the form of updating PCA subspace, which is guided by internally motivated Q-learning. By equipped with the proposed algorithm, robot makes nest 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 robot to learning new knowledge, acquiring knowledge and developing intelligence in a online and incremental manner.
cognitive development internal motivation visual novelty online PCA Q-learning
Xinyu Qu Minghai Yao
College of Information Engineering Zhejiang University of Technology Hangzhou, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1482-1487
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）