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. 08, 2023

Filed:

Jun. 28, 2018
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Eric Jang, Cupertino, CA (US);

Sudheendra Vijayanarasimhan, Pasadena, CA (US);

Peter Pastor Sampedro, Oakland, CA (US);

Julian Ibarz, Mountain View, CA (US);

Sergey Levine, Berkeley, CA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B25J 9/16 (2006.01); G06N 3/08 (2023.01); G06N 3/008 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
B25J 9/163 (2013.01); G06N 3/008 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G05B 2219/39536 (2013.01);
Abstract

Deep machine learning methods and apparatus related to semantic robotic grasping are provided. Some implementations relate to training a training a grasp neural network, a semantic neural network, and a joint neural network of a semantic grasping model. In some of those implementations, the joint network is a deep neural network and can be trained based on both: grasp losses generated based on grasp predictions generated over a grasp neural network, and semantic losses generated based on semantic predictions generated over the semantic neural network. Some implementations are directed to utilization of the trained semantic grasping model to servo, or control, a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).


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