Robert Boschgmbh

Max-planck-gesellschaft Zur Forderung Der Wissenschaften E.v.

The Duy Nguyen-Tuong

Christoph Zimmer

Heiner Markert

Martin Schiegg

Ernst Kloppenburg

Michael Hanselmann

Volker Imhof

Mona Meister

Christian Daniel

Andreas Doerr

Bastian Bischoff

Sebastian Trimpe

Hon Sum Alec Yu

Jens Schreiter

Peter Cromme

Felix Eberhard Hildebrand

Dingling Yao

Heiner Storck

Valerie Bouvier

Gunnar Picht

Alexander Konstandin

Training a Gaussian process state space model, which describes a correlation between selected control parameters of a plurality of control parameters for controlling a robotic device and output variables of the robotic device assigned in each case. The method includes: for each control parameter: ascertaining a transitional normal distribution and an output variable normal distribution assigned to a respective predicted output state, ascertaining a respective value of a piece of mutual information between the ascertained output variable normal distribution and the ascertained transitional normal distribution; selecting the control parameter having the highest value of the mutual information as the new control parameter; controlling the robotic device using the new control parameter and measuring an output variable of the robotic device assigned to the new control parameter; and training the Gaussian process state space model using the new control parameter and the assigned measured output variable of the robotic device.

Device and method for training a gaussian process state space model

A computer-implemented method for the safe, active training of a computer-aided model for modeling time series of a physical system using Gaussian processes, including the steps of establishing a safety threshold value &#x3b1;; initializing by implementing safe initial curves as input values on the system, creating an initial regression model and an initial safety model; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section; implementing the determined new curve section on the physical system and measuring output variables; incorporating the new output values in the regression model and the safety model until N passes have been carried out; and outputting the regression model and the safety model.

Method for the safe training of a dynamic model

A method for ascertaining a time characteristic of a measured variable adjustable by an actuator, wherein a time characteristic of a control variable is applied to the actuator, wherein the ascertaining is effected by means of a Gaussian process state model of the behavior of the actuator, wherein the time characteristic of the measured variable of the actuator is ascertained on the basis of a parameterizable family of functions, wherein in the parameterizable family of functions a time dependency of a later latent state, in particular ascertained using a transfer function, of the actuator on an earlier latent state of the actuator and an earlier control variable of the actuator is the same as the applicable dependency of the Gaussian process state model.

Method for ascertaining a time characteristic of a measured variable, prediction system, actuator control system, method for training the actuator control system, training system, computer program, and machine-readable storage medium

A method for generating an allowed input data trajectory for a physical system to be tested or measured, including providing an input data trajectory in an input data space; determining an allowed operating range; and replacing at least one segment of the provided input data trajectory, which is outside of the allowed operating range, by a trajectory segment within the allowed operating range, in order to obtain the allowed input data trajectory.

Method and device for generating allowed input data trajectories for a testing system

A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points.

Method and apparatus for supplying interpolation point data for a data-based function model calculation unit

Growing community of inventors

Leonberg, Germany

The Duy Nguyen-Tuong