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:
Apr. 30, 2024

Filed:

Mar. 22, 2019
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Pararth Shah, Sunnyvale, CA (US);

Dilek Hakkani-Tur, Los Altos, CA (US);

Juliana Kew, San Francisco, CA (US);

Marek Fiser, Mountain View, CA (US);

Aleksandra Faust, Palo Alto, CA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/008 (2023.01); B25J 9/16 (2006.01); B25J 13/08 (2006.01); G05B 13/02 (2006.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01); G06F 18/21 (2023.01); G06N 3/044 (2023.01); G06T 7/593 (2017.01); G06V 20/10 (2022.01); G06V 30/262 (2022.01); G10L 15/16 (2006.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01); G10L 25/78 (2013.01);
U.S. Cl.
CPC ...
G06N 3/008 (2013.01); B25J 9/161 (2013.01); B25J 9/162 (2013.01); B25J 9/163 (2013.01); B25J 9/1697 (2013.01); B25J 13/08 (2013.01); G05B 13/027 (2013.01); G05D 1/0221 (2013.01); G06F 18/21 (2023.01); G06N 3/044 (2023.01); G06T 7/593 (2017.01); G06V 20/10 (2022.01); G06V 30/274 (2022.01); G10L 15/16 (2013.01); G10L 15/1815 (2013.01); G10L 15/22 (2013.01); G10L 25/78 (2013.01); G10L 2015/223 (2013.01);
Abstract

Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.


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