Learning task-relevant features from robot data

Feature extraction from robot sensor data is a standard way to deal with the high dimensionality and redundancy of such data. In order to get optimal task-relevant features, PCA must be replaced by a supervised projection method. In this paper we extend our previously proposed supervised linear feature extraction method (2000) in two ways: 1) the projection matrix is optimized simultaneously over all columns under the constraint of orthonormality; and 2) a Jacobi parametrization of the matrix allows the use of unconstrained nonlinear optimization algorithms. The new algorithm is more efficient and many times faster than the old version. We show experimental results in extracting features from panoramic images of a mobile robot. The results compare favorably to the PCA solutions.