Resumes
Resumes
Claire Delaunay Phones & Addresses
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1320 Modoc Ave, Menlo Park, CA 94025
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Redwood City, CA
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Palo Alto, CA
Publications
Us Patents
Remote Robotic Presence
View pageUS Patent:
20140015914, Jan 16, 2014
Filed:
Jul 12, 2013
Appl. No.:
13/941029
Inventors:
Claire Delaunay - Palo Alto CA, US
International Classification:
H04N 7/15
US Classification:
348 1402, 901 50
Abstract:
A mobile robot system for video teleconferencing system is described herein. A robot can be operable by a remote user. Via the robot, a remote user can interact within a local user's environment to provide telepresence capabilities. For example, the robot can be deployed on a horizontal surface, such as a table or desktop. The robot can include a microcontroller, a drive unit, and interface to a consumer device, such as a mobile device. The drive unit can include two or more motors for providing motion capabilities. The microcontroller can be wired or communicatively coupled to the consumer device. In general, the consumer device may be a mobile phone or a tablet computer where processing power and wireless or other capabilities of these devices can be utilized by the system. The system can be based on a networking protocol providing multi-party data exchanges.
Training, Testing, And Verifying Autonomous Machines Using Simulated Environments
View pageUS Patent:
20230004801, Jan 5, 2023
Filed:
Aug 30, 2022
Appl. No.:
17/898887
Inventors:
- Santa Clara CA, US
John Zedlewski - San Francisco CA, US
Zachary Taylor - Santa Cruz CA, US
Greg Heinrich - Nice, FR
Claire Delaunay - Menlo Park CA, US
Mark Daly - Eagle ID, US
Matthew Campbell - Surf City NC, US
Curtis Beeson - Irwin PA, US
Gary Hicok - Mesa CA, US
Michael Cox - Menlo Park CA, US
Rev Lebaredian - Los Gatos CA, US
Tony Tamasi - Portola Valley CA, US
David Auld - Saratoga CA, US
John Zedlewski - San Francisco CA, US
Zachary Taylor - Santa Cruz CA, US
Greg Heinrich - Nice, FR
Claire Delaunay - Menlo Park CA, US
Mark Daly - Eagle ID, US
Matthew Campbell - Surf City NC, US
Curtis Beeson - Irwin PA, US
Gary Hicok - Mesa CA, US
Michael Cox - Menlo Park CA, US
Rev Lebaredian - Los Gatos CA, US
Tony Tamasi - Portola Valley CA, US
David Auld - Saratoga CA, US
International Classification:
G06N 3/08
G06N 20/00
G06F 9/455
G06N 3/063
G06K 9/62
G06N 3/04
G06V 10/44
G06V 20/56
G06N 20/00
G06F 9/455
G06N 3/063
G06K 9/62
G06N 3/04
G06V 10/44
G06V 20/56
Abstract:
In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.
Training, Testing, And Verifying Autonomous Machines Using Simulated Environments
View pageUS Patent:
20190303759, Oct 3, 2019
Filed:
Mar 27, 2019
Appl. No.:
16/366875
Inventors:
- San Jose CA, US
John Zedlewski - San Francisco CA, US
Zachary Taylor - Santa Cruz CA, US
Greg Heinrich - Nice, FR
Claire Delaunay - Menlo Park CA, US
Mark Daly - Eagle ID, US
Matthew Campbell - Surf City NC, US
Curtis Beeson - Irwin PA, US
Gary Hicok - Mesa AZ, US
Michael Cox - Menlo Park CA, US
Rev Lebaredian - Los Gatos CA, US
Tony Tamasi - Portola Valley CA, US
David Auld - Saratoga CA, US
John Zedlewski - San Francisco CA, US
Zachary Taylor - Santa Cruz CA, US
Greg Heinrich - Nice, FR
Claire Delaunay - Menlo Park CA, US
Mark Daly - Eagle ID, US
Matthew Campbell - Surf City NC, US
Curtis Beeson - Irwin PA, US
Gary Hicok - Mesa AZ, US
Michael Cox - Menlo Park CA, US
Rev Lebaredian - Los Gatos CA, US
Tony Tamasi - Portola Valley CA, US
David Auld - Saratoga CA, US
International Classification:
G06N 3/08
G06F 9/455
G06N 20/00
G06F 9/455
G06N 20/00
Abstract:
In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.