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    MACHINE LEARNING METHODS AND APPARATUS FOR SEMANTIC ROBOTIC GRASPING
    2.
    发明申请
    MACHINE LEARNING METHODS AND APPARATUS FOR SEMANTIC ROBOTIC GRASPING 审中-公开

    公开(公告)号：US20200338722A1

    公开(公告)日：2020-10-29

    申请号：US16622309

    申请日：2018-06-28

    Applicant: Google LLC

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

    IPC: B25J9/16 ,  G06N3/04 ,  G06N3/08

    Abstract: Deep machine learning methods and apparatus related to semantic robotic grasping are provided. Some implementations relate to training a training a grasp neural network, a semantic neural network, and a joint neural network of a semantic grasping model. In some of those implementations, the joint network is a deep neural network and can be trained based on both: grasp losses generated based on grasp predictions generated over a grasp neural network, and semantic losses generated based on semantic predictions generated over the semantic neural network. Some implementations are directed to utilization of the trained semantic grasping model to servo, or control, a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).

    Deep machine learning methods and apparatus for robotic grasping
    3.
    发明授权
    Deep machine learning methods and apparatus for robotic grasping 有权

    公开(公告)号：US11548145B2

    公开(公告)日：2023-01-10

    申请号：US17172666

    申请日：2021-02-10

    Applicant: Google LLC

    Inventor： Sergey Levine ,  Peter Pastor Sampedro ,  Alex Krizhevsky

    IPC: G06F17/00 ,  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.

    DEEP MACHINE LEARNING METHODS AND APPARATUS FOR ROBOTIC GRASPING
    5.
    发明申请
    DEEP MACHINE LEARNING METHODS AND APPARATUS FOR ROBOTIC GRASPING 有权

    公开(公告)号：US20210162590A1

    公开(公告)日：2021-06-03

    申请号：US17172666

    申请日：2021-02-10

    Applicant: Google LLC

    Inventor： Sergey Levine ,  Peter Pastor Sampedro ,  Alex Krizhevsky

    IPC: 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.

    Deep machine learning methods and apparatus for robotic grasping
    6.
    发明授权
    Deep machine learning methods and apparatus for robotic grasping 有权

    公开(公告)号：US10946515B2

    公开(公告)日：2021-03-16

    申请号：US16234272

    申请日：2018-12-27

    Applicant: Google LLC

    Inventor： Sergey Levine ,  Peter Pastor Sampedro ,  Alex Krizhevsky

    IPC: G06F17/00 ,  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.

    DEEP MACHINE LEARNING METHODS AND APPARATUS FOR ROBOTIC GRASPING
    8.
    发明申请
    DEEP MACHINE LEARNING METHODS AND APPARATUS FOR ROBOTIC GRASPING 审中-公开

    公开(公告)号：US20190283245A1

    公开(公告)日：2019-09-19

    申请号：US16234272

    申请日：2018-12-27

    Applicant: Google LLC

    Inventor： Sergey Levine ,  Peter Pastor Sampedro ,  Alex Krizhevsky

    IPC: B25J9/16 ,  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.

    DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
    10.
    发明申请
    DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION 有权

    公开(公告)号：US20210237266A1

    公开(公告)日：2021-08-05

    申请号：US17052679

    申请日：2019-06-14

    Applicant: Google LLC

    Inventor： Dmitry Kalashnikov ,  Alexander Irpan ,  Peter Pastor Sampedro ,  Julian Ibarz ,  Alexander Herzog ,  Eric Jang ,  Deirdre Quillen ,  Ethan Holly ,  Sergey Levine

    IPC: B25J9/16 ,  G06N3/08

    Abstract: Using large-scale reinforcement learning to train a policy model that can be utilized by a robot in performing a robotic task in which the robot interacts with one or more environmental objects. In various implementations, off-policy deep reinforcement learning is used to train the policy model, and the off-policy deep reinforcement learning is based on self-supervised data collection. The policy model can be a neural network model. Implementations of the reinforcement learning utilized in training the neural network model utilize a continuous-action variant of Q-learning. Through techniques disclosed herein, implementations can learn policies that generalize effectively to previously unseen objects, previously unseen environments, etc.

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