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:
Aug. 31, 2021

Filed:

Jul. 03, 2017
Applicants:

Krishna Shankar, Los Altos, CA (US);

Nicolas Hudson, San Mateo, CA (US);

Alexander Toshev, San Francisco, CA (US);

Inventors:

Krishna Shankar, Los Altos, CA (US);

Nicolas Hudson, San Mateo, CA (US);

Alexander Toshev, San Francisco, CA (US);

Assignee:

X DEVELOPMENT LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06N 3/00 (2006.01); G06K 9/62 (2006.01); B25J 9/16 (2006.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); B25J 9/161 (2013.01); B25J 9/1605 (2013.01); B25J 9/1671 (2013.01); G06K 9/6254 (2013.01); G06N 3/008 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); Y10S 901/03 (2013.01);
Abstract

Methods, apparatus, and computer-readable media for determining and utilizing corrections to robot actions. Some implementations are directed to updating a local features model of a robot in response to determining a human correction of an action performed by the robot. The local features model is used to determine, based on an embedding generated over a corresponding neural network model, one or more features that are most similar to the generated embedding. Updating the local features model in response to a human correction can include updating a feature embedding, of the local features model, that corresponds to the human correction. Adjustment(s) to the features model can immediately improve robot performance without necessitating retraining of the corresponding neural network model.


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