Robert Boschgmbh

Nicolai Waniek

Philipp Christian Schillinger

Mathias Buerger

Andras Gabor Kupcsik

Markus Spies

Marco Todescato

Meng Guo

Leonel Rozo

Patrick Kesper

Markus Giftthaler

David Adrian

A device and a method for training an image generator. The method includes: providing an image sequence that includes an image for each time of a plurality of times; training a first encoder, a second encoder, and a decoder by: for each of a number of times of the plurality of times: for the image assigned to the time, producing a multiplicity of feature maps for the image by a neural network and grouping them into first and second subsets; supplying the first subset to the first encoder to produce first feature vector; supplying the second subset to the second encoder to produce a second feature vector; supplying the first feature vector and the second feature vector to the decoder to produce a predicted target image; producing an error value; and adapting the first encoder, the second encoder, and the decoder to reduce the error value.

Device and method for training an image generator

A method for training a machine learning model to recognize an object topology of an object from an image of the object. The method includes: obtaining a 3D model of the object; determining a descriptor component value for each vertex of the grid; generating training data image pairs each having a training input image and a target image. The target image is generated by determining the vertex positions in the training input image; assigning the descriptor component value determined for the vertex at the vertex position to the position in the target image; and adapting at least some of the descriptor component values assigned to the positions in the target image or adding descriptor component values to the positions of the target image.

Apparatus and method for training a machine learning model to recognize an object topology of an object from an image of the object

A method for controlling a robot to pick up an object in various positions. The method includes: defining a plurality of reference points on the object; mapping a first camera image of the object in a known position onto a first descriptor image; identifying the descriptors of the reference points from the first descriptor image; mapping a second camera image of the object in an unknown position onto a second descriptor image; searching the identified descriptors of the reference points in the second descriptor image; ascertaining the positions of the reference points in the three-dimensional space in the unknown position from the found positions; and ascertaining a pickup pose of the object for the unknown position from the ascertained positions of the reference points.

Device and method for controlling a robot to pick up an object in various positions

A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.

Method and system for robot manipulation planning

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Dornstadt, Germany

Nicolai Waniek