Abstract: A method for monitoring during a robot-assisted first or second process-includes (a.1) detecting process data; and (a.2) performing a model-based assessment with the aid of a machine-learned model on the basis of the detected process data; wherein, if the model-based assessment satisfies an examination criterion, in particular depending on an external confirmation: (b.1) performing a test assessment with the aid of a testing authority; and (b.2) training the machine-learned model further on the basis of the test assessment; and then, for the first process optionally performed again: (c.1) detecting process data; (c.2) performing the model-based assessment with the aid of the further trained model on the basis of the detected process data; and (c.3) monitoring during the first process is performed on the basis of this assessment.

Abstract: A method for handling a load arrangement with a robot includes: activating a lifting state of a gripper of the robot for load lifting; determining a parameter of a time profile of a load arrangement-dependent force variable using at least one sensor of the robot during a movement of the lifted load arrangement; classifying a load arrangement lifted by the gripper using a machine-learned model on the basis of the determined parameter, in particular during a movement of the lifted load arrangement and/or over the pick-up area in which the load arrangement has been situated for lifting, in particular a pick-up area of a pick-up station and/or over or in a pick-up container; and at least one of the steps of: carrying out a first process with the robot if the load arrangement has been classified into a first class; and/or carrying out a second process with the robot if the load arrangement has been classified into a second class.

Abstract: A method for executing a process, in particular using at least one robot, includes executing a run-through of the process, detecting a value of a first process variable, and detecting an assessment of this executed process run-through. Assessment learning steps are then repeated multiple times, wherein run-throughs of the process using varied process controls are executed and additional assessments are detected. A first quality factor model of the process, which model determines a quality factor for the process on the basis on the first process variable, is machine-learned based on the detected assessments and values of the first process variable. The method further includes repeating process control optimization steps multiple times.

Abstract: A method for carrying out an application using at least one robot includes, repeatedly ascertaining a stochastic value of at least one robot parameter and/or at least one environmental model parameter; and carrying out a simulation of the application on the basis of the ascertained stochastic value, training at least one control agent and/or at least one classification agent using the simulations by machine learning, and carrying out the application using the robot. The method may further include configuring a controller of the robot, by means of which the application is carried out wholly or in part, based on the trained control agent, and/or classifying the application using the trained classification agent.

Abstract: A method for analyzing and/or configuring an industrial installation, which has at least one first installation component for capturing, handling and/or machining at least one first object. A process success of the first installation component is predicted and/or a value for a configuration parameter of the first installation component is determined on the basis of at least one first object model of the first object with the aid of at least one first machine-learned component model of the first installation component.