公开(公告)号：US11941504B2

公开(公告)日：2024-03-26

申请号：US17040299

申请日：2019-03-22

Applicant: Google LLC

Inventor： Pararth Shah ,  Dilek Hakkani-Tur ,  Juliana Kew ,  Marek Fiser ,  Aleksandra Faust

IPC: G06N3/008 ,  B25J9/16 ,  B25J13/08 ,  G05B13/02 ,  G05D1/00 ,  G05D1/02 ,  G06F18/21 ,  G06N3/044 ,  G06T7/593 ,  G06V20/10 ,  G06V30/262 ,  G10L15/16 ,  G10L15/18 ,  G10L15/22 ,  G10L25/78

CPC classification number: G06N3/008 ,  B25J9/161 ,  B25J9/162 ,  B25J9/163 ,  B25J9/1697 ,  B25J13/08 ,  G05B13/027 ,  G05D1/0221 ,  G06F18/21 ,  G06N3/044 ,  G06T7/593 ,  G06V20/10 ,  G06V30/274 ,  G10L15/16 ,  G10L15/1815 ,  G10L15/22 ,  G10L25/78 ,  G10L2015/223

Abstract: Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.

公开(公告)号：US20210086353A1

公开(公告)日：2021-03-25

申请号：US17040299

申请日：2019-03-22

Applicant: Google LLC

Inventor： Pararth Shah ,  Dilek Hakkani-Tur ,  Juliana Kew ,  Marek Fiser ,  Aleksandra Faust

IPC: B25J9/16 ,  G10L25/78 ,  G10L15/22 ,  G10L15/18 ,  G06K9/00 ,  G06K9/62 ,  G10L15/16 ,  G06T7/593 ,  G06K9/72 ,  B25J13/08 ,  G05D1/02 ,  G05B13/02 ,  G06N3/04

Abstract: Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.

公开(公告)号：US20240249109A1

公开(公告)日：2024-07-25

申请号：US18601159

申请日：2024-03-11

Applicant: GOOGLE LLC

Inventor： Pararth Shah ,  Dilek Hakkani-Tur ,  Juliana Kew ,  Marek Fiser ,  Aleksandra Faust

IPC: G06N3/008 ,  B25J9/16 ,  B25J13/08 ,  G05B13/02 ,  G06F18/21 ,  G06N3/044 ,  G06T7/593 ,  G06V20/10 ,  G06V30/262 ,  G10L15/16 ,  G10L15/18 ,  G10L15/22 ,  G10L25/78

CPC classification number: G06N3/008 ,  B25J9/161 ,  B25J9/162 ,  B25J9/163 ,  B25J9/1697 ,  B25J13/08 ,  G05B13/027 ,  G06F18/21 ,  G06N3/044 ,  G06T7/593 ,  G06V20/10 ,  G06V30/274 ,  G10L15/16 ,  G10L15/1815 ,  G10L15/22 ,  G10L25/78 ,  G10L2015/223

Abstract: Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.

公开(公告)号：US11972339B2

公开(公告)日：2024-04-30

申请号：US17040299

申请日：2019-03-22

Applicant: Google LLC

Inventor： Pararth Shah ,  Dilek Hakkani-Tur ,  Juliana Kew ,  Marek Fiser ,  Aleksandra Faust

IPC: G06N3/008 ,  B25J9/16 ,  B25J13/08 ,  G05B13/02 ,  G05D1/00 ,  G05D1/02 ,  G06F18/21 ,  G06N3/044 ,  G06T7/593 ,  G06V20/10 ,  G06V30/262 ,  G10L15/16 ,  G10L15/18 ,  G10L15/22 ,  G10L25/78

CPC classification number: G06N3/008 ,  B25J9/161 ,  B25J9/162 ,  B25J9/163 ,  B25J9/1697 ,  B25J13/08 ,  G05B13/027 ,  G05D1/0221 ,  G06F18/21 ,  G06N3/044 ,  G06T7/593 ,  G06V20/10 ,  G06V30/274 ,  G10L15/16 ,  G10L15/1815 ,  G10L15/22 ,  G10L25/78 ,  G10L2015/223

Abstract: Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.