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.

Date of Patent:
Dec. 26, 2023

Filed:

Aug. 23, 2022
Applicant:

Google Llc, Mountain View, CA (US);

Inventor:

Pierre Sermanet, Palo Alto, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 19/04 (2006.01); G06N 3/084 (2023.01); B25J 9/16 (2006.01); G05B 13/02 (2006.01); G06V 10/70 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); H04N 7/18 (2006.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); B25J 9/163 (2013.01); B25J 9/1697 (2013.01); G05B 13/027 (2013.01); G06V 10/70 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); H04N 7/181 (2013.01);
Abstract

This description relates to a neural network that has multiple network parameters and is configured to receive an input observation characterizing a state of an environment and to process the input observation to generate a numeric embedding of the state of the environment. The neural network can be used to control a robotic agent. The network can be trained using a method comprising: obtaining a first observation captured by a first modality; obtaining a second observation that is co-occurring with the first observation and that is captured by a second, different modality; obtaining a third observation captured by the first modality that is not co-occurring with the first observation; determining a gradient of a triplet loss that uses the first observation, the second observation, and the third observation; and updating current values of the network parameters using the gradient of the triplet loss.


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