Robert Boschgmbh

Alexander Kuss

Philipp Christian Schillinger

Zohar Feldman

Anh Vien Ngo

Miroslav Gabriel

Hanna Ziesche

A method for picking up an object by means of a robotic device. The method includes obtaining at least one depth image of the object; determining, for each of a plurality of points of the object, the value of a measure of the scattering of surface normal vectors in an area around the point of the object; supplying the determined values to a neural network configured to output, in response to an input containing measured scattering values, an indication of object locations for pick-up; determining a location of the object for pick-up from an output which the neural network outputs in response to the supply of the determined values; and controlling the robotic device to pick up the object at the determined location.

Method for picking up an object by means of a robotic device

A method for ascertaining object information from image data. The method includes training an agent with the aid of reinforcement learning, successively recording images according to actions that are output by the agent, after each recording, the agent obtaining information, generated from the previously recorded images, concerning the location of surface points of an object as state information, and ascertaining the object information from the recorded images with the aid of the machine learning model.

Method for ascertaining object information with the aid of image data

A robot device, a method for training a robot control model, and a method for controlling a robot device. The method for training includes: supplying an image showing object(s), to a first and second prediction model to produce a first and second pickup prediction that has, for each pixel of the image, a first and second pickup robot configuration vector with an assigned first and second success probability; supplying the first and second pickup prediction to a blending model of the robot control model to produce a third pickup prediction that has, for each pixel of the image: a third pickup robot configuration vector that is a weighted combination of the first and second pickup robot configuration vector, and a third success probability that is a weighted combination of the first and second success probability; and training the robot control model by adapting the first and second weighting factors.

Robot device, method for the computer-implemented training of a robot control model, and method for controlling a robot device

A method for training a neural network for controlling a robot. The method includes ascertaining a camera pose in a robot cell and an uncertainty area around the ascertained camera pose, ascertaining an object area in the robot cell, generating training-camera-images, for each training-camera-image a training-camera-image camera pose being randomly established in the uncertainty area around the ascertained camera pose, a training-camera-image object pose being randomly established in the object area and the training-camera-image being generated, so that it shows the object with the training-camera-image object pose from the perspective of a camera with the training-camera-image camera pose, generating training data from the training-camera-images, each training-camera-image being assigned one or multiple training-robot control parameters for handling the object in the training-camera-image object pose of the training-camera-image, and training the neural network using the training data.

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Schoenaich, Germany

Alexander Kuss