Patent ID: 11940774
Assignee: UBTECH ROBOTICS CORP LTD
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. The robot of claim 13, wherein the instructions for training the to-be-trained second neural network model based on the first sample two-dimensional image, the first sample two-dimensional coordinates and the first pairing relationship comprise:
instructions for inputting the first sample two-dimensional image into the to-be-trained second neural network model, to train the to-be-trained second neural network model;
instructions for obtaining, second sample two-dimensional coordinates of each key point of a sample to-be-imitated object in the sample two-dimensional image, and a second pairing relationship between key points of the sample to-be-imitated object, and wherein the second sample two-dimensional coordinates and the second pairing relationship are outputted by the to-be-trained second neural network model;
instructions for determining second sample two-dimensional coordinates matching the first sample two-dimensional coordinates based on the first pairing relationship and the second pairing relationship; and
instructions for calculating a first loss function value of the first sample two-dimensional coordinates and the second sample two-dimensional coordinates;
instructions for determining whether the first loss function value is less than a first threshold and a change rate of the first loss function value is less than a second threshold; and
instructions for, in response to the first loss function value not being less than the first threshold or the change rate of the first loss function value not being less than the second threshold, returning to perform the step of inputting the first sample two-dimensional image into the to-be-trained second neural network model, until when the first loss function value is less than the first threshold and the change rate of the first loss function value is less than the second threshold, stopping the training, and obtaining the pre-trained second neural network model.