公开(公告)号：US20230098284A1

公开(公告)日：2023-03-30

申请号：US17935496

申请日：2022-09-26

申请人： Robert Bosch GmbH

发明人： Andras Gabor Kupcsik ,  Philipp Christian Schillinger ,  Alexander Kuss ,  Anh Vien Ngo ,  Miroslav Gabriel ,  Zohar Feldman

IPC分类号： G06V10/774 ,  G06V10/82 ,  G06V10/22

摘要： A method for generating training data for supervised learning for training a neural network to identify, from digital images of objects, locations of the objects for interacting with the objects. The method includes: acquiring, for each training object, at least one digital reference image and a plurality of further images of the training object; for each training object, specifying a location of the training object, mapping the at least one reference image onto a descriptor image, identifying descriptors of the specified location, mapping the further images of the training object onto further descriptor images, and determining locations in the further images by locating points in the further images, the descriptors of which in the further descriptor images correspond to the specified descriptors of the at least one specified location; and generating the training data for supervised learning by marking the determined locations for the further images of the training objects.

公开(公告)号：US20230114306A1

公开(公告)日：2023-04-13

申请号：US17934887

申请日：2022-09-23

申请人： Robert Bosch GmbH

发明人： Alexander Kuss ,  Anh Vien Ngo ,  Miroslav Gabriel ,  Philipp Christian Schillinger ,  Zohar Feldman

IPC分类号： G06T7/50 ,  B25J9/16

摘要： 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.

公开(公告)号：US20230063799A1

公开(公告)日：2023-03-02

申请号：US17893596

申请日：2022-08-23

申请人： Robert Bosch GmbH

发明人： Anh Vien Ngo ,  Alexander Kuss ,  Hanna Ziesche ,  Miroslav Gabriel ,  Philipp Christian Schillinger ,  Zohar Feldman

IPC分类号： B25J9/16 ,  G06T7/10

摘要： 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.

公开(公告)号：US20220301209A1

公开(公告)日：2022-09-22

申请号：US17698703

申请日：2022-03-18

申请人： Robert Bosch GmbH

发明人： Alexander Kuss

IPC分类号： G06T7/55 ,  G06K9/62 ,  G06T7/70

摘要： 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.