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：US20220143819A1

公开(公告)日：2022-05-12

申请号：US17094521

申请日：2020-11-10

Applicant: Google LLC

Inventor： Jie Tan ,  Sehoon Ha ,  Peng Xu ,  Sergey Levine ,  Zhenyu Tan

IPC: B25J9/16 ,  B25J13/08 ,  G06N3/08

Abstract: Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.

公开(公告)号：US11992945B2

公开(公告)日：2024-05-28

申请号：US17094521

申请日：2020-11-10

Applicant: Google LLC

Inventor： Jie Tan ,  Sehoon Ha ,  Peng Xu ,  Sergey Levine ,  Zhenyu Tan

IPC: B25J9/16 ,  B25J13/08 ,  G05D1/00 ,  G06N3/08

CPC classification number: B25J9/163 ,  B25J9/162 ,  B25J9/1689 ,  B25J13/089 ,  G05D1/02 ,  G06N3/08

Abstract: Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.

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.