Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis
WANG Xiaofeng①② ZHANG Minglu① LIU Jun②
①(School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China) ②(Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System, Tianjin 300384, China)
Abstract：Existing Candid Covariance-free Incremental PCA (CCIPCA) has the limitation of the stable image inherent covariance, and a Generalized CCIPCA (GCCIPCA) with an appended term of the mean difference vector is presented. It can be considered that the CCIPCA is only a special case of the GCCIPCA and can extend the scope of the algorithm. Then, the incremental learning of the proposed GCCIPCA is innovated to the existing Bi-Directional PCA (BDPCA), and the called Incremental BDPCA (IBDPCA) is used for the robot perceptual learning and it can be used to incrementally compute the principal components without estimating the similar scatter matrixes in the row and column directions, which can build up the real-time processing speed greatly. Finally, the blocks grasped by the robot are used as the perceptual objects, and the experimental results demonstrate that the proposed algorithm works well, and the convergence rate, the classification recognition rate, the computation time and the required memory are improved significantly.
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