A method of assuring the quality of a process, wherein the method comprises the following steps, which are repeated a plurality of times: - carrying out the process; - acquiring learning process data in the course of the carrying out of the process; and - acquiring a learning evaluation score of the process that has been carried out; the following step: - machine learning a model of the process on the basis of the acquired learning process data and learning evaluation scores; and which comprises the following steps, in particular the following steps repeated a plurality of times: - carrying out the process another time; and - acquiring current process data in the course of the carrying out the process another time; wherein, on the basis of the current process data and of the machine-learned model: - a success of the process that has been carried out another time is determined, in particular classified, and - as a function of the determined success of the process that has been carried out another time, further processing and / or transporting of a workpiece that is being handled, in particular processed, in the process that has been carried out another time, is carried out in the case of fault-free processing, and re-processing or ejecting is carried out in the case of faulty processing; and wherein the process is at least partially carried out by at least one robot, and the current process data and / or the learning process data comprise robot-specific actual data and / or target data, in particular actual and / or target data detected by sensors on the robot side and / or sensors external to the robot, wherein the robot-specific current process data and / or the robot-specific learning process data comprise - poses of one or more robot-fixed references, in particular of a TCP, - poses of one or more robot axes, and / or - first and / or higher time derivatives thereof; and / or - forces on at least one robot-fixed reference and / or drive variables of robot drives. - carrying out the process; - acquiring learning process data in the course of the carrying out of the process; and - acquiring a learning evaluation score of the process that has been carried out; the following step: - machine learning a model of the process on the basis of the acquired learning process data and learning evaluation scores; - machine learning a model of the process on the basis of the acquired learning process data and learning evaluation scores; and which comprises the following steps, in particular the following steps repeated a plurality of times: - carrying out the process another time; and - acquiring current process data in the course of the carrying out the process another time; - carrying out the process another time; and - acquiring current process data in the course of the carrying out the process another time; wherein, on the basis of the current process data and of the machine-learned model: - a success of the process that has been carried out another time is determined, in particular classified, and - as a function of the determined success of the process that has been carried out another time, further processing and / or transporting of a workpiece that is being handled, in particular processed, in the process that has been carried out another time, is carried out in the case of fault-free processing, and re-processing or ejecting is carried out in the case of faulty processing; and - a success of the process that has been carried out another time is determined, in particular classified, and - as a function of the determined success of the process that has been carried out another time, further processing and / or transporting of a workpiece that is being handled, in particular processed, in the process that has been carried out another time, is carried out in the case of fault-free processing, and re-processing or ejecting is carried out in the case of faulty processing; and wherein the process is at least partially carried out by at least one robot, and the current process data and / or the learning process data comprise robot-specific actual data and / or target data, in particular actual and / or target data detected by sensors on the robot side and / or sensors external to the robot, wherein the robot-specific current process data and / or the robot-specific learning process data comprise - poses of one or more robot-fixed references, in particular of a TCP, - poses of one or more robot axes, and / or - first and / or higher time derivatives thereof; and / or - forces on at least one robot-fixed reference and / or drive variables of robot drives. - poses of one or more robot-fixed references, in particular of a TCP, - poses of one or more robot axes, and / or - first and / or higher time derivatives thereof; and / or - forces on at least one robot-fixed reference and / or drive variables of robot drives.