Abstract: A method for controlling a robot using control parameter values from a non-Euclidean control parameter space. The method includes performing a Bayesian optimization of an objective function representing a desired control objective of the robot over the control parameter space, wherein evaluation points of the objective function are determined by searching an optimum of an acquisition function in an iterative search. In each iteration, the following are performed: updating a candidate evaluation point using a search direction in the tangent space of the parameter space at the candidate evaluation point, mapping the updated candidate evaluation point from the tangent space to the parameter space, and using the mapped updated candidate evaluation point as evaluation point for a next iteration until a stop criterion is fulfilled, and controlling the robot in accordance with a control parameter value found in the Bayesian optimization.

Abstract: A method for controlling a robot using control parameter values from a non-Euclidean original control parameter space. The method includes performing a Bayesian optimization of an objective function representing a desired control objective of the robot over the original control parameter space; and controlling the robot in accordance with a control parameter value from the original control parameter space found in the Bayesian optimization. The Bayesian optimization includes: Transforming the original control parameter space to a reduced control parameter space using the observed control parameter values, the original control parameter space comprises a first number of dimensions, the reduced control parameter space comprises a second number of dimensions, and the first number of dimensions is higher than the second number of dimensions; Determining an evaluation point of the objective function in the reduced control parameter space by searching an optimum of an acquisition function in an iterative search.

Abstract: A method for controlling a robot using control parameter values from a non-Euclidean control parameter space. The method includes performing a Bayesian optimization of an objective function representing a desired control objective of the robot over the control parameter space, wherein evaluation points of the objective function are determined by searching an optimum of an acquisition function in an iterative search. In each iteration, the following are performed: updating a candidate evaluation point using a search direction in the tangent space of the parameter space at the candidate evaluation point, mapping the updated candidate evaluation point from the tangent space to the parameter space, and using the mapped updated candidate evaluation point as evaluation point for a next iteration until a stop criterion is fulfilled, and controlling the robot in accordance with a control parameter value found in the Bayesian optimization.