A method of specifying an actuation pattern command lexicon comprising at least one actuation pattern for inputting a robot command, wherein at least one actuation pattern of the actuation pattern command lexicon has a progression over time of robot data and / or threshold values for robot data; wherein the method comprises the steps of: - specifying a robot command which is to be learnt, on the basis of an input from an operator with the aid of an input / output means of a human-machine interface; - applying an actuation pattern of at least one member of the robot by manually guiding the robot by the operator; - acquiring robot data of this actuation pattern, which robot data comprise values of a progression over time of at least one pose of a reference fixed with respect to the robot and / or a progression over time of at least one time derivative of a pose of a reference fixed with respect to the robot and / or a progression over time of at least one force at a reference fixed with respect to the robot and / or a progression over time of at least one drive variable of the robot, wherein a start signal and / or a stop signal of the acquisition of the robot data is output to an operator; - machine learning the actuation pattern in a model on the basis of the acquired robot data, wherein the model assigns a first actuation pattern learned by machine learning to a first robot command; and - assigning (S40; S60) the actuation pattern learned by machine learning to the robot command; characterised in that a plurality of sequences of robot data for inputting the same robot command are acquired, wherein these sequences comprise sequences effected by different operators, wherein robot data that are specified by the operator in the course of the actuation and to which the robot command should not be assigned are also used specifically for learning the actuation pattern. wherein the method comprises the steps of: - specifying a robot command which is to be learnt, on the basis of an input from an operator with the aid of an input / output means of a human-machine interface; - applying an actuation pattern of at least one member of the robot by manually guiding the robot by the operator; - acquiring robot data of this actuation pattern, which robot data comprise values of a progression over time of at least one pose of a reference fixed with respect to the robot and / or a progression over time of at least one time derivative of a pose of a reference fixed with respect to the robot and / or a progression over time of at least one force at a reference fixed with respect to the robot and / or a progression over time of at least one drive variable of the robot, wherein a start signal and / or a stop signal of the acquisition of the robot data is output to an operator; - machine learning the actuation pattern in a model on the basis of the acquired robot data, wherein the model assigns a first actuation pattern learned by machine learning to a first robot command; and - assigning (S40; S60) the actuation pattern learned by machine learning to the robot command; - specifying a robot command which is to be learnt, on the basis of an input from an operator with the aid of an input / output means of a human-machine interface; - applying an actuation pattern of at least one member of the robot by manually guiding the robot by the operator; - acquiring robot data of this actuation pattern, which robot data comprise values of a progression over time of at least one pose of a reference fixed with respect to the robot and / or a progression over time of at least one time derivative of a pose of a reference fixed with respect to the robot and / or a progression over time of at least one force at a reference fixed with respect to the robot and / or a progression over time of at least one drive variable of the robot, wherein a start signal and / or a stop signal of the acquisition of the robot data is output to an operator; - machine learning the actuation pattern in a model on the basis of the acquired robot data, wherein the model assigns a first actuation pattern learned by machine learning to a first robot command; and - assigning (S40; S60) the actuation pattern learned by machine learning to the robot command; characterised in that a plurality of sequences of robot data for inputting the same robot command are acquired, wherein these sequences comprise sequences effected by different operators, wherein robot data that are specified by the operator in the course of the actuation and to which the robot command should not be assigned are also used specifically for learning the actuation pattern.