Abstract: A method for training a behavior planner for an at least partially self-driving target vehicle on the basis of observation data regarding kinematics and/or dynamics that have been recorded during at least one test drive in a test vehicle includes identifying a driving maneuver that moves the test vehicle from an initial state to an end state using the observation data, ascertaining the maneuver end time, retrieving a maneuver duration required by the target vehicle to perform the identified driving maneuver from a dynamics model of the target vehicle, labeling observation data from a time interval, defined by the maneuver duration, with the identified driving maneuver, and training the behavior planner, using the labeled observation data, to map observation data that indicate a state of the target vehicle to at least one driving maneuver to be performed.

Abstract: A method for predicting the trajectories of extraneous objects in the surroundings of an ego vehicle, and for determining a separate future trajectory adapted thereto for the ego vehicle. The method includes identifying the extraneous objects are identified. It is ascertained toward which proximate destination the movement of each of the extraneous objects is headed and according to which basic rules this movement occurs. It is ascertained toward which proximate destination the movement of the ego vehicle is headed and according to which basic rules this movement occurs. One quality function each is established for the ego vehicle and the extraneous objects. One quality measure each is established for the ego vehicle as well as for the extraneous objects. Those optimal movement strategies of the ego vehicle and of the extraneous objects that maximize the quality measure are ascertained. The sought trajectories are ascertained from the optimal movement strategies.

Abstract: A method for controlling a robot is described. The method includes acquiring sensor data representing an environment of the robot, identifying one or more objects in the environment of the robot from the sensor data, associating the robot and each of the one or more objects with a respective agent of a multiagent system, determining, for each agent of the multiagent system, a quality measure which includes a reward term for a movement action at a position and a coupling term which depends on the probabilities of the other agents occupying the same position as the agent at a time, determining a movement policy of the robot that selects movement actions with a higher value of the quality measure determined for the robot with higher probability than movement actions with a lower value of the quality measure, and controlling the robot according to the movement policy.

Abstract: A method for controlling a robot is described. The method includes acquiring sensor data representing an environment of the robot, identifying one or more objects in the environment of the robot from the sensor data, associating the robot and each of the one or more objects with a respective agent of a multiagent system, determining, for each agent of the multiagent system, a quality measure which includes a reward term for a movement action at a position and a coupling term which depends on the probabilities of the other agents occupying the same position as the agent at a time, determining a movement policy of the robot that selects movement actions with a higher value of the quality measure determined for the robot with higher probability than movement actions with a lower value of the quality measure, and controlling the robot according to the movement policy.

Abstract: A computer-implemented method for performing Learning from Demonstrations, particularly Imitation Learning, based on data associated with a first domain, particularly a source domain. The method includes: determining first data characterizing a demonstrator of the first domain, wherein particularly the first data characterizes sensor data of the demonstrator and/or sensor data of at least one spectator observing the demonstrator, determining first knowledge from the first domain based on the first data, transferring at least a part of the first knowledge to a second domain, particularly a target domain.

Abstract: A method for predicting the trajectories of extraneous objects in the surroundings of an ego vehicle, and for determining a separate future trajectory adapted thereto for the ego vehicle. The method includes identifying the extraneous objects are identified. It is ascertained toward which proximate destination the movement of each of the extraneous objects is headed and according to which basic rules this movement occurs. It is ascertained toward which proximate destination the movement of the ego vehicle is headed and according to which basic rules this movement occurs. One quality function each is established for the ego vehicle and the extraneous objects. One quality measure each is established for the ego vehicle as well as for the extraneous objects. Those optimal movement strategies of the ego vehicle and of the extraneous objects that maximize the quality measure are ascertained. The sought trajectories are ascertained from the optimal movement strategies.