Robert Boschgmbh

Felix Schmitt

Michael Hanselmann

Holger Andreas Banzhaf

Herke Van Hoof

Jan Guenter Woehlke

Johannes Maximilian Doellinger

Johannes Goth

Martin Stoll

A method for controlling an agent. The method includes obtaining numerical values of a first and second set of state variables, which together represent a current full state of the agent, and the numerical values of the first set of state variables represent a current partial state of the robot; determining a state value prior comprising, for potential subsequent partial states following the current partial state, an evaluation of the subsequent partial states in terms of achieving a goal to be attained by the agent; supplying an input comprising a local crop of the state value prior and the numerical values of the second set of state variables representing, together with the numerical values of the first set of state variables, the current full state to a neural network configured to output an evaluation of control actions and controlling the agent in accordance with control signals.

Device and method for controlling an agent

A method of training a control strategy for a control. An exploration strategy for a current version of the control strategy is determined in each of several iterations. Several simulation runs are carried out, in each of which an action is selected in accordance with the exploration strategy, and it being checked if the selected action is safe, until a safe action has been selected or a maximum number of actions greater than or equal to two has been selected. A follow-up state of the state in the sequence of states is ascertained. The sequence of states are collected as data of the simulation run; for the iteration. The value of a loss function is ascertained over the data of the executed simulation runs and the control strategy is adapted so that the value of the loss function is reduced.

Device and method for training a control strategy for a control device over several iterations

A method for controlling a robot. The method includes receiving an indication of a target configuration to be reached from an initial configuration of the robot, determining a coarse-scale value map by value iteration, starting from an initial coarse-scale state and until the robot reaches the target configuration or a maximum number of fine-scale states has been reached, determining a fine-scale sub-goal from the coarse-scale value map, performing, by an actuator of the robot, fine-scale control actions to reach the determined fine-scale sub-goal and obtaining sensor data to determine the fine-scale states reached, starting from a current fine-scale state of the robot and until the robot reaches the determined fine-scale sub-goal, the robot transitions to a different coarse-scale state, or a maximum sequence length of the sequence of fine-scale states has been reached and determining the next coarse-scale state.

Device and method for controlling a robot

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Ludwigsburg, Germany

Felix Schmitt