Abstract: A method for training a control policy for a robot device includes acquiring a reference state of an environment of the robot device and a reference observation of the environment for the reference state. The method also includes generating, for each of a plurality of errors of an estimation of a pose of the robot device, an observation that is disturbed with respect to the reference observation according to the error of the pose estimation and a training data element comprising the generated observation as a training input. The method further includes training the control policy using the generated training data elements.

Abstract: A method for controlling a robot device. The method includes acquiring an image(s) of in a workspace of the robot device; determining, by a neural network, object hierarchy information specifying stacking relations of the objects with respect to each other in the workspace of the robot device and confidence information for the object hierarchy information from the image(s); if the confidence information indicates a confidence above a confidence threshold, manipulating an object of the objects; if the confidence information indicates a confidence lower than the confidence threshold, acquiring an additional image of the objects and determining, by the neural network, additional object hierarchy information specifying stacking relations of the objects with respect to each other in the workspace of the robot device and additional confidence information for the additional object hierarchy information from the additional image and control the robot using the additional object hierarchy information.

Abstract: A computer-implemented method for providing training data for training of a data-driven depth completion model as a machine-learning model, wherein the depth completion model is to be trained to generate dense depth maps from sensor acquired raw depth maps. The method includes: providing multiple synthetic dense depth map data items from CAD data of various synthetic scenes; providing multiple real raw depth map data items obtained from real-world depth sensor measurements of real-world scenes; training a generative model for obtaining a trained generator model for generating generated raw depth map data items from the synthetic dense depth map data items; applying the trained generator model to generate training data from provided synthetic dense depth map data.