Robert Boschgmbh

Hon Sum Alec Yu

Christoph Zimmer

The Duy Nguyen-Tuong

Dingling Yao

Training a Gaussian process state space model, which describes a correlation between selected control parameters of a plurality of control parameters for controlling a robotic device and output variables of the robotic device assigned in each case. The method includes: for each control parameter: ascertaining a transitional normal distribution and an output variable normal distribution assigned to a respective predicted output state, ascertaining a respective value of a piece of mutual information between the ascertained output variable normal distribution and the ascertained transitional normal distribution; selecting the control parameter having the highest value of the mutual information as the new control parameter; controlling the robotic device using the new control parameter and measuring an output variable of the robotic device assigned to the new control parameter; and training the Gaussian process state space model using the new control parameter and the assigned measured output variable of the robotic device.

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London, United Kingdom

Hon Sum Alec Yu