公开(公告)号：US11436441B2

公开(公告)日：2022-09-06

申请号：US16717471

申请日：2019-12-17

Applicant: Google LLC

Inventor： Jie Tan ,  Sehoon Ha ,  Tingnan Zhang ,  Xinlei Pan ,  Brian Andrew Ichter ,  Aleksandra Faust

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G05D1/00

Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.

公开(公告)号：US20210182620A1

公开(公告)日：2021-06-17

申请号：US16717471

申请日：2019-12-17

Applicant: Google LLC

Inventor： Jie Tan ,  Sehoon Ha ,  Tingnan Zhang ,  Xinlei Pan ,  Brian Andrew Ichter ,  Aleksandra Faust

IPC: G06K9/62 ,  G05D1/00 ,  G06N3/08 ,  G06N3/04

Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.