公开(公告)号：US20240058954A1

公开(公告)日：2024-02-22

申请号：US17890783

申请日：2022-08-18

Applicant: GOOGLE LLC

Inventor： Matthew Bennice ,  Paul Bechard ,  Joséphine Simon ,  Jiayi Lin

IPC: B25J9/16 ,  B25J19/02

CPC classification number: B25J9/163 ,  B25J9/161 ,  B25J9/1671 ,  B25J19/022

Abstract: Implementations are provided for training robot control policies using augmented reality (AR) sensor data comprising physical sensor data injected with virtual objects. In various implementations, physical pose(s) of physical sensor(s) of a physical robot operating in a physical environment may be determined. Virtual pose(s) of virtual object(s) in the physical environment may also be determined. Based on the physical poses virtual poses, the virtual object(s) may be injected into sensor data generated by the one or more physical sensors to generate AR sensor data. The physical robot may be operated in the physical environment based on the AR sensor data and a robot control policy. The robot control policy may be trained based on virtual interactions between the physical robot and the one or more virtual objects.

公开(公告)号：US20240253215A1

公开(公告)日：2024-08-01

申请号：US18104001

申请日：2023-01-31

Applicant: GOOGLE LLC

Inventor： Matthew Bennice ,  Paul Bechard ,  Joséphine Simon ,  Jiayi Lin

IPC: B25J9/16

CPC classification number: B25J9/163 ,  B25J9/161 ,  B25J9/1671

Abstract: Implementations are provided for training a robot control policy for controlling a robot. During a first training phase, the robot control policy is trained using a first set of training data that includes (i) training data generated based on simulated operation of the robot in a first fidelity simulation, and (ii) training data generated based on simulated operation of the robot in a second fidelity simulation, wherein the second fidelity is greater than the first fidelity. When one or more criteria for commencing a second training phase are satisfied, the robot control policy is further trained using a second set of training data that also include training data generate based on simulated operation of the robot in the first and second fidelity simulations, which has a ratio therebetween lower than that in the first set of training data.