US 2025/0128409 A1 · Robotic Systems and Methods Used to Update Training of a Neural Network Based upon Neural Network Outputs

Abstract

A robotic system for use in installing final trim and assembly part includes an auto-labeling system that combines images of a primary component, such as a vehicle, with those of computer based model, where feature based object tracking methods are used to compare the two. In some forms a camera can be mounted to a moveable robot, while in other the camera can be fixed in position relative to the robot. An artificial marker can be used in some forms. Robot movement tracking can also be used. A runtime operation can utilize a deep learning network to augment feature-based object tracking to aid in initializing a pose of the vehicle as well as an aid in restoring tracking if lost.

Claims20

Dependent claims

A method to train a neural network using heat map derived feedback, the method comprising:

initializing a neural network for a training procedure, the neural network structured to determine a pose of a manufacturing component in a testing image, each pose defined by a six dimensional pose which includes three rotations about separate axes and three translations along the separate axes;
providing a set of training images to be used in training the neural network, each image in the set of training images including an associated pose;
setting a block location in which an occlusion will reside in each image of the set of images when the neural network is trained;
adding a block to the block location in the set of training images;
and
training the neural network using an error between a pose of a training image and the estimated pose of the training image provided by the neural network in light of the block added to each image in the set of training images.

3 more elements

An apparatus to update a neural network based upon a heatmap evaluation of a test image, the apparatus comprising:

a collection of training images, each of image of the training images paired with an associated pose of a manufacturing component, each pose defined by a six dimensional pose which includes three rotations about separate axes and three translations along the separate axes;
a controller structured to train the neural network and configured to perform the following:
initialize the neural network for a training procedure to be conducted with the collection of training images;
receive a command to set a block location in which an occlusion will reside in each image of the collection of training images when the neural network is trained;
add a block to the block location in the collection of training images;
and
train the neural network using an error between a pose of a training image and the estimated pose of the training image provided by the neural network in light of the block added to each image in the collection of training images.

4 more elements

Dependencies are inferred from the claim text.

Robotic Systems and Methods Used to Update Training of a Neural Network Based upon Neural Network Outputs

Life of this patent

Robotic Systems and Methods Used to Update Training of a Neural Network Based upon Neural Network Outputs

Life of this patent

Abstract

Claims20