公开(公告)号：US20170252924A1

公开(公告)日：2017-09-07

申请号：US15448013

申请日：2017-03-02

Applicant: Google Inc.

Inventor： Sudheendra Vijayanarasimhan ,  Eric Jang ,  Peter Pastor Sampedro ,  Sergey Levine

IPC: B25J9/16 ,  G06N3/08

CPC classification number: B25J9/163 ,  B25J9/1612 ,  B25J9/1697 ,  G05B13/027 ,  G05B19/18 ,  G06N3/008 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/084 ,  Y10S901/36

Abstract: Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).

公开(公告)号：US10207402B2

公开(公告)日：2019-02-19

申请号：US15377280

申请日：2016-12-13

Applicant: Google Inc.

Inventor： Sergey Levine ,  Peter Pastor Sampedro ,  Alex Krizhevsky

IPC: G06F19/00 ,  B25J9/16 ,  G05B13/02 ,  G06N3/04 ,  G06N3/08

Abstract: Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a deep neural network to predict a measure that candidate motion data for an end effector of a robot will result in a successful grasp of one or more objects by the end effector. Some implementations are directed to utilization of the trained deep neural network to servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector. For example, the trained deep neural network may be utilized in the iterative updating of motion control commands for one or more actuators of a robot that control the pose of a grasping end effector of the robot, and to determine when to generate grasping control commands to effectuate an attempted grasp by the grasping end effector.

公开(公告)号：US20170334066A1

公开(公告)日：2017-11-23

申请号：US15596103

申请日：2017-05-16

Applicant: Google Inc.

Inventor： Sergey Levine ,  Chelsea Finn ,  Ian Goodfellow

IPC: B25J9/16

CPC classification number: B25J9/161 ,  B25J9/1656 ,  B25J9/1697 ,  G05B13/027 ,  G05B2219/39289 ,  G06K9/00 ,  G06K9/00335 ,  G06K9/4628 ,  G06N3/008 ,  G06N3/0454 ,  G06N3/084 ,  G06T7/74 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30164

Abstract: Some implementations of this specification are directed generally to deep machine learning methods and apparatus related to predicting motion(s) (if any) that will occur to object(s) in an environment of a robot in response to particular movement of the robot in the environment. Some implementations are directed to training a deep neural network model to predict at least one transformation (if any), of an image of a robot's environment, that will occur as a result of implementing at least a portion of a particular movement of the robot in the environment. The trained deep neural network model may predict the transformation based on input that includes the image and a group of robot movement parameters that define the portion of the particular movement.

公开(公告)号：US20170252922A1

公开(公告)日：2017-09-07

申请号：US15377280

申请日：2016-12-13

Applicant: Google Inc.

Inventor： Sergey Levine ,  Peter Pastor Sampedro ,  Alex Krizhevsky

IPC: B25J9/16

CPC classification number: B25J9/161 ,  B25J9/1612 ,  B25J9/1664 ,  B25J9/1697 ,  G05B13/027 ,  G06N3/0454 ,  G06N3/084

Abstract: Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a deep neural network to predict a measure that candidate motion data for an end effector of a robot will result in a successful grasp of one or more objects by the end effector. Some implementations are directed to utilization of the trained deep neural network to servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector. For example, the trained deep neural network may be utilized in the iterative updating of motion control commands for one or more actuators of a robot that control the pose of a grasping end effector of the robot, and to determine when to generate grasping control commands to effectuate an attempted grasp by the grasping end effector.

公开(公告)号：US11173599B2

公开(公告)日：2021-11-16

申请号：US15596103

申请日：2017-05-16

Applicant: Google Inc.

Inventor： Sergey Levine ,  Chelsea Finn ,  Ian Goodfellow

IPC: G06N3/08 ,  G06N3/00 ,  G06N3/04 ,  B25J9/16 ,  G05B13/02 ,  G06K9/46 ,  G06K9/00 ,  G06T7/73

Abstract: Some implementations of this specification are directed generally to deep machine learning methods and apparatus related to predicting motion(s) (if any) that will occur to object(s) in an environment of a robot in response to particular movement of the robot in the environment. Some implementations are directed to training a deep neural network model to predict at least one transformation (if any), of an image of a robot's environment, that will occur as a result of implementing at least a portion of a particular movement of the robot in the environment. The trained deep neural network model may predict the transformation based on input that includes the image and a group of robot movement parameters that define the portion of the particular movement.

公开(公告)号：US09914213B2

公开(公告)日：2018-03-13

申请号：US15448013

申请日：2017-03-02

Applicant: Google Inc.

Inventor： Sudheendra Vijayanarasimhan ,  Eric Jang ,  Peter Pastor Sampedro ,  Sergey Levine

IPC: G05B19/04 ,  G05B19/18 ,  B25J9/16 ,  G06N3/08 ,  G05B13/02 ,  G06N3/04

CPC classification number: B25J9/163 ,  B25J9/1612 ,  B25J9/1697 ,  G05B13/027 ,  G05B19/18 ,  G06N3/008 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/084 ,  Y10S901/36

Abstract: Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).