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.

公开(公告)号：US20240190004A1

公开(公告)日：2024-06-13

申请号：US18582266

申请日：2024-02-20

Applicant: GOOGLE LLC

Inventor： Yunfei Bai ,  Tigran Gasparian ,  Brent Austin ,  Andreas Christiansen ,  Matthew Bennice ,  Paul Bechard

IPC: B25J9/16

CPC classification number: B25J9/1671 ,  B25J9/1661 ,  B25J9/1697

Abstract: Active utilization of a robotic simulator in control of one or more real world robots. A simulated environment of the robotic simulator can be configured to reflect a real world environment in which a real robot is currently disposed, or will be disposed. The robotic simulator can then be used to determine a sequence of robotic actions for use by the real world robot(s) in performing at least part of a robotic task. The sequence of robotic actions can be applied, to a simulated robot of the robotic simulator, to generate a sequence of anticipated simulated state data instances. The real robot can be controlled to implement the sequence of robotic actions. The implementation of one or more of the robotic actions can be contingent on a real state data instance having at least a threshold degree of similarity to a corresponding one of the anticipated simulated state data instances.

公开(公告)号：US11938638B2

公开(公告)日：2024-03-26

申请号：US17337815

申请日：2021-06-03

Applicant: GOOGLE LLC

Inventor： Yunfei Bai ,  Tigran Gasparian ,  Brent Austin ,  Andreas Christiansen ,  Matthew Bennice ,  Paul Bechard

IPC: B25J9/16

CPC classification number: B25J9/1671 ,  B25J9/1661 ,  B25J9/1697

Abstract: Active utilization of a robotic simulator in control of one or more real world robots. A simulated environment of the robotic simulator can be configured to reflect a real world environment in which a real robot is currently disposed, or will be disposed. The robotic simulator can then be used to determine a sequence of robotic actions for use by the real world robot(s) in performing at least part of a robotic task. The sequence of robotic actions can be applied, to a simulated robot of the robotic simulator, to generate a sequence of anticipated simulated state data instances. The real robot can be controlled to implement the sequence of robotic actions. The implementation of one or more of the robotic actions can be contingent on a real state data instance having at least a threshold degree of similarity to a corresponding one of the anticipated simulated state data instances.

公开(公告)号：US20230381970A1

公开(公告)日：2023-11-30

申请号：US18233251

申请日：2023-08-11

Applicant: GOOGLE LLC

Inventor： Seyed Mohammad Khansari Zadeh ,  Eric Jang ,  Daniel Lam ,  Daniel Kappler ,  Matthew Bennice ,  Brent Austin ,  Yunfei Bai ,  Sergey Levine ,  Alexander Irpan ,  Nicolas Sievers ,  Chelsea Finn

IPC: B25J9/16 ,  B25J13/06

CPC classification number: B25J9/1697 ,  B25J9/163 ,  B25J9/1661 ,  B25J9/161 ,  B25J13/06

Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

公开(公告)号：US11772272B2

公开(公告)日：2023-10-03

申请号：US17203296

申请日：2021-03-16

Applicant: GOOGLE LLC

Inventor： Seyed Mohammad Khansari Zadeh ,  Eric Jang ,  Daniel Lam ,  Daniel Kappler ,  Matthew Bennice ,  Brent Austin ,  Yunfei Bai ,  Sergey Levine ,  Alexander Irpan ,  Nicolas Sievers ,  Chelsea Finn

IPC: B25J9/16 ,  B25J13/06

CPC classification number: B25J9/1697 ,  B25J9/161 ,  B25J9/163 ,  B25J9/1661 ,  B25J13/06

Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.