公开(公告)号：US12165021B2

公开(公告)日：2024-12-10

申请号：US17307507

申请日：2021-05-04

Applicant: Google LLC

Inventor： Nareshkumar Rajkumar ,  Patrick Leger

IPC: G06N20/00 ,  B25J9/16 ,  G06F16/28 ,  G06N5/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, a system receives classification examples from a plurality of remote devices over a communication network. The classification examples can include (i) a data representation generated by a remote device based on sensor data captured by the remote device and (ii) a classification corresponding to the data representation. The system assigns quality scores to the classification examples based on a level of similarity of the data representations with other data representations. The system selects a subset of the classification examples based on the quality scores assigned to the classification examples. The system trains a machine learning model using the selected subset of the classification examples.

公开(公告)号：US12265910B2

公开(公告)日：2025-04-01

申请号：US17930874

申请日：2022-09-09

Applicant: Google LLC

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

IPC: G06F13/00 ,  B25J9/00 ,  B25J9/16 ,  G06N3/08 ,  H04L67/12 ,  H04L67/568

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.