公开(公告)号：US20240042600A1

公开(公告)日：2024-02-08

申请号：US18331632

申请日：2023-06-08

Applicant: DeepMind Technologies Limited

Inventor： Serkan Cabi ,  Ziyu Wang ,  Alexander Novikov ,  Ksenia Konyushkova ,  Sergio Gomez Colmenarejo ,  Scott Ellison Reed ,  Misha Man Ray Denil ,  Jonathan Karl Scholz ,  Oleg O. Sushkov ,  Rae Chan Jeong ,  David Barker ,  David Budden ,  Mel Vecerik ,  Yusuf Aytar ,  Joao Ferdinando Gomes de Freitas

IPC: B25J9/16

CPC classification number: B25J9/161 ,  B25J9/163 ,  B25J9/1661

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

公开(公告)号：US11712799B2

公开(公告)日：2023-08-01

申请号：US17020294

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

Inventor： Serkan Cabi ,  Ziyu Wang ,  Alexander Novikov ,  Ksenia Konyushkova ,  Sergio Gomez Colmenarejo ,  Scott Ellison Reed ,  Misha Man Ray Denil ,  Jonathan Karl Scholz ,  Oleg O. Sushkov ,  Rae Chan Jeong ,  David Barker ,  David Budden ,  Mel Vecerik ,  Yusuf Aytar ,  Joao Ferdinando Gomes de Freitas

IPC: B25J9/16

CPC classification number: B25J9/161 ,  B25J9/163 ,  B25J9/1661

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

公开(公告)号：US20210078169A1

公开(公告)日：2021-03-18

申请号：US17020294

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

Inventor： Serkan Cabi ,  Ziyu Wang ,  Alexander Novikov ,  Ksenia Konyushkova ,  Sergio Gomez Colmenarejo ,  Scott Ellison Reed ,  Misha Man Ray Denil ,  Jonathan Karl Scholz ,  Oleg O. Sushkov ,  Rae Chan Jeong ,  David Barker ,  David Budden ,  Mel Vecerik ,  Yusuf Aytar ,  Joao Ferdinando Gomes de Freitas

IPC: B25J9/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.