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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12112494 B1

PDFFull Text
Date of Patent:
Oct. 08, 2024

Filed:

Feb. 28, 2020

Robotic manipulation using domain-invariant 3d representations predicted from 2.5d vision data

Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Honglak Lee, Mountain View, CA (US);

Xinchen Yan, Cupertino, CA (US);

Soeren Pirk, Palo Alto, CA (US);

Yunfei Bai, Fremont, CA (US);

Seyed Mohammad Khansari Zadeh, San Carlos, CA (US);

Yuanzheng Gong, San Jose, CA (US);

Jasmine Hsu, San Francisco, CA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:

Gray Ice Higdon

Primary Examiner:

B M M Hannan

Assistant Examiner:

Alyzia N Dilworth

Int. Cl.
CPC ...
G06T 7/55 (2017.01); B25J 9/16 (2006.01); B25J 13/08 (2006.01); G06F 18/21 (2023.01); G06T 7/50 (2017.01); G06V 20/10 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06T 7/55 (2017.01); B25J 9/1605 (2013.01); B25J 9/163 (2013.01); B25J 9/1669 (2013.01); B25J 9/1697 (2013.01); B25J 13/08 (2013.01); G06F 18/2163 (2023.01); G06T 7/50 (2017.01); G06V 20/10 (2022.01); G06V 20/64 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01);
Abstract

Implementations relate to training a point cloud prediction model that can be utilized to process a single-view two-and-a-half-dimensional (2.5D) observation of an object, to generate a domain-invariant three-dimensional (3D) representation of the object. Implementations additionally or alternatively relate to utilizing the domain-invariant 3D representation to train a robotic manipulation policy model using, as at least part of the input to the robotic manipulation policy model during training, the domain-invariant 3D representations of simulated objects to be manipulated. Implementations additionally or alternatively relate to utilizing the trained robotic manipulation policy model in control of a robot based on output generated by processing generated domain-invariant 3D representations utilizing the robotic manipulation policy model.

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