Robert Boschgmbh

Markus Spies

Andras Gabor Kupcsik

Philipp Christian Schillinger

Mathias Buerger

Nicolai Waniek

Marco Todescato

Leonel Rozo

Meng Guo

Luigi Palmieri

Kai Oliver Arras

Markus Giftthaler

Johannes Maximilian Doellinger

Patrick Kesper

Liangcheng Fu

Raphael Kusumoto Barbosa De Almeida

A method for training a machine learning model for recognizing an object topology of an object from an image of the object. The method includes obtaining a 3D model of the object, wherein the 3D model comprises a mesh of vertices connected by edges, wherein each edge has a weight which specifies proximity of two vertices connected by the edge in the object; determining a descriptor for each vertex of the mesh by searching descriptors for the vertices which minimize the sum, over pairs of connected vertices, of distances between the descriptors of the pair of vertices weighted by the weight of the edge between the pair of vertices; generating training data image pairs, wherein each training data image pair comprises a training input image showing the object and a target image; and training the machine learning model by supervised learning using the training data image pairs as training data.

Device and method for training a machine learning model for recognizing an object topology of an object from an image of the object

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 ascertaining the pose of an object. The method includes recording a first and a second camera image of the object, ascertaining a correspondence between camera pixels of the camera images and vertices of a 3D model of the object, and ascertaining the pose of the object from a set of poses by minimizing, across the set of poses, a loss function, the loss function for a pose being provided by accumulation of distance measures between projections of the object in the pose onto the respective camera image plane and the corresponding pixels of the respective camera image.

Device and method for ascertaining the pose of an object

A method of controlling a vehicle or robot. The method includes the following steps: determining a first control sequence, determining a second control sequence for controlling the vehicle or robot depending on the first control sequence, a current state of the vehicle or robot, and on a model characterizing a dynamic behavior of the vehicle or robot, controlling the vehicle or robot depending on the second control sequence, wherein the determining of the first control sequence is performed depending on a first candidate control sequence and a second candidate control sequence.

Method of controlling a vehicle and apparatus for controlling a vehicle

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

A method for tracking multiple target objects, moving objects to be tracked being projected onto a grid map having grid cells, the method including the following tasks to be executed in each time: computing the velocity distribution for the next time step with the aid of a transition velocity distribution, which indicates how the objects associated with a grid cell in question move from one time step to the next, based on the preceding velocity distribution; for each grid cell, calculating a transitional probability information item, which indicates, for objects in each grid cell, probabilities of the objects in question reaching possible, further grid cells, as a function of the velocity distribution; calculating an occupancy probability for each grid cell for a subsequent time, based on the transitional probability information item; operating a system as a function of the occupancy probabilities for the grid cells.

Method for tracking multiple target objects, device, and computer program for implementing the tracking of multiple target objects for the case of moving objects

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Karlsruhe, Germany

Markus Spies