公开(公告)号：US10730181B1

公开(公告)日：2020-08-04

申请号：US15855393

申请日：2017-12-27

Applicant: X Development LLC

Inventor： Nareshkumar Rajkumar ,  Patrick Leger ,  Abhinav Gupta

IPC: B25J9/16 ,  B25J9/00 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer-readable media storing executable instructions, for enhancing robot learning. In some implementations, a robot stores first embeddings generated using a first machine learning model, and the first embeddings include one or more first private embeddings that are not shared with other robots. The robot receives a second machine learning model from a server system over a communication network. The robot generates a second private embedding for each of the one or more first private embeddings using the second machine learning model. The robot adds the second private embeddings to the cache of the robot and removes the one or more first private embeddings from the cache of the robot.

公开(公告)号：US20230004802A1

公开(公告)日：2023-01-05

申请号：US17930874

申请日：2022-09-09

Applicant: X Development LLC

Inventor： Nareshkumar Rajkumar ,  Patrick Leger ,  Nicolas Hudson ,  Krishna Shankar ,  Rainer Hessmer

IPC: G06N3/08 ,  B25J9/00 ,  B25J9/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.

公开(公告)号：US10967509B2

公开(公告)日：2021-04-06

申请号：US16910163

申请日：2020-06-24

Applicant: X Development LLC

Inventor： Nareshkumar Rajkumar ,  Patrick Leger ,  Abhinav Gupta

IPC: G06N20/00 ,  B25J9/16 ,  B25J9/00

Abstract: Methods, systems, and apparatus, including computer-readable media storing executable instructions, for enhancing robot learning. In some implementations, a robot stores first embeddings generated using a first machine learning model, and the first embeddings include one or more first private embeddings that are not shared with other robots. The robot receives a second machine learning model from a server system over a communication network. The robot generates a second private embedding for each of the one or more first private embeddings using the second machine learning model. The robot adds the second private embeddings to the cache of the robot and removes the one or more first private embeddings from the cache of the robot.

公开(公告)号：US20190197396A1

公开(公告)日：2019-06-27

申请号：US15855329

申请日：2017-12-27

Applicant: X Development LLC

Inventor： Nareshkumar Rajkumar ,  Patrick Leger ,  Nicolas Hudson ,  Krishna Shankar ,  Rainer Hessmer

IPC: G06N3/08

CPC classification number: G06N3/08 ,  B25J9/0003 ,  B25J9/1671 ,  G05B2219/45108 ,  H04L67/12 ,  H04L67/2842

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.