Sensor fusion for human-robot skill transfer systems

This paper describes how to design a data fusion module in a skill transfer system. The data fusion paradigm is addressed. It consists of two independent modules for optimal fusion and filtering. A new interpretation of the Kalman filter equations is done, to achieve a 'model-free' equation capable of following arbitrary variables. An engineering approach is used to tune the parameters of interest for a certain task. The fusion algorithm presented here is global and can easily be extended to any arbitrary system. It was successfully tested in a human-robot skill transfer of the peg-in-hole task at the DLR.

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