TRAINING OF ARTIFICIAL INTELLIGENCE MODEL

1 . A computer-implemented method comprising:

causing, based on a representation of a part of an object, a robot to position an image capturing device relative to the part, the representation indicating a first characteristic of the object;

receiving a first image generated by the image capturing device while the image capturing device is positioned relative to the part;

generating a first input to a machine learning model based on the first image;

determining a first output of the machine learning model based on the first input, the first output indicating a second characteristic different than the first characteristic;

generating an updated representation that indicates the second characteristic in place of the first characteristic; and

causing the robot to perform an operation on the object based on the updated representation.

2 . The computer-implemented method of claim 1 , further comprising:

generating information for updating the representation to indicate the second characteristic rather than the first characteristic based on the first output of the machine learning model;

transmitting the information to an external server; and

receiving the updated representation from the external server.

3 . The computer-implemented method of claim 2 , further comprising:

determining an offset between the first characteristic and the second characteristic;

determining that the offset exceeds a threshold offset; and

determining to generate the information for updating the representation based on determining that the offset exceeds the threshold offset.

4 . The computer-implemented method of claim 3 , further comprising:

including the first image and the offset in a first training data, wherein the machine learning model training is based on a loss function that minimizes the offset.

5 . The computer-implemented method of claim 1 , further comprising:

generating first training data based on the first characteristic and the second characteristic; and

storing the first training data for machine learning model training.

6 . The computer-implemented method of claim 1 , wherein the first characteristic is associated with a first pose characteristic of the part and the second characteristic is associated with a second pose characteristic of the part, and wherein the method further comprises:

determining a pose of a target include in the part based on the second pose characteristic;

generating instructions for an operation to be performed by the robot based on the pose; and

causing the robot to perform the operation based on the instructions.

7 . The computer-implemented method of claim 6 , further comprising:

causing, based on causing the robot to perform the operation, the robot to reposition the image capturing device relative to the part;

receiving a second image generated by the image capturing device while the image capturing device is repositioned relative to the part;

generating a second input to a machine learning model based on the second image;

determining a second output of the machine learning model based on the second input; and

generating information for generating a representation of the part based on the second output of the machine learning model.

8 . The computer-implemented method of claim 1 , further comprising:

generating a synthetic image that shows at least one modeled target that corresponds to at least one target of the part;

generating training data based on the synthetic image; and

storing the training data for the machine learning model training.

9 . The computer-implemented method of claim 8 , wherein the synthetic image is generated by using an image transformation applied to a modeled target.

10 . The computer-implemented method of claim 8 further comprising:

training, using the synthetic image the machine learning model to determine one of position information or a type of a target shown in the synthetic image.

11 . The computer-implemented method of claim 10 , wherein the object comprises an aircraft and the target comprises a fastener.

12 . The computer-implemented method of claim 1 further comprising:

causing the robot to position the image capturing device relative to a second target included in the part;

causing the image capturing device to capture a second image showing the second target;

determining a corrected visual inspection property of the second target; and

including, in training data for the machine learning model, the second image and the corrected visual inspection property.

13 . A system comprising:

one or more processors; and

one or more computer-readable media having stored thereon instructions that, upon executed, cause the system to:

cause, based on a representation of a part of an object, a robot to position an image capturing device relative to the part, the representation indicating a first characteristic of the object;

receive a first image generated by the image capturing device while the image capturing device is positioned relative to the part;

generate a first input to a machine learning model based on the first image;

determine a first output of the machine learning model based on the first input, the first output indicating a second characteristic different than the first characteristic;

generate information for updating the representation to indicate the second characteristic rather than the first characteristic based on the first output of the machine learning model;

generate an updated representation that indicates the second characteristic in place of the first characteristic; and

cause the robot to perform an operation on the object based on the updated representation.

14 . The system of claim 13 , wherein the instructions that, upon executed, further cause the system to:

generating information for updating the representation to indicate the second characteristic rather than the first characteristic based on the first output of the machine learning model;

transmitting the information to an external server; and

receiving the updated representation from the external server.

15 . The system of claim 13 , wherein the instructions that, upon executed, further cause the system to:

determine an offset between the first characteristic and the second characteristic;

determine that the offset exceeds a threshold offset; and

determine to generate the information for updating the representation based on determining that the offset exceeds the threshold offset.

16 . The system of claim 15 , wherein the instructions that, upon executed, further cause the system to:

include the first image and the offset in a first training data, wherein the machine learning model training is based on a loss function that minimizes the offset.

17 . The system of claim 13 , wherein the instructions that, upon executed, further cause the system to:

generate first training data based on the first characteristic and the second characteristic; and

store the first training data for machine learning model training.

18 . The system of claim 13 , wherein the first characteristic is associated with a first pose characteristic of the part and the second characteristic is associated with a second pose characteristic of the part, and wherein the instructions that, upon executed, further cause the system to:

determine a pose of a target include in the part based on the second pose characteristic;

generate instructions for an operation to be performed by the robot based on the pose; and

cause the robot to perform the operation based on the instructions.

19 . One or more non-transitory computer-readable media having stored thereon instructions that, when executed, cause a system to:

cause, based on a representation of a part of an object, a robot to position an image capturing device relative to the part, the representation indicating a first characteristic of the object;

receive a first image generated by the image capturing device while the image capturing device is positioned relative to the part;

generate a first input to a machine learning model based on the first image;

determine a first output of the machine learning model based on the first input, the first output indicating a second characteristic different than the first characteristic;

generate information for updating the representation to indicate the second characteristic rather than the first characteristic based on the first output of the machine learning model;

generate an updated representation that indicates the second characteristic in place of the first characteristic; and

cause the robot to perform an operation on the object based on the updated representation.

20 . The one or more non-transitory computer-readable media of claim 19 , wherein the instructions that, upon executed, further cause the system to:

determine an offset between the first characteristic and the second characteristic;

determine that the offset exceeds a threshold offset; and

determine to generate the information for updating the representation based on determining that the offset exceeds the threshold offset.