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    Deep reinforcement learning for robotic manipulation
    3.
    发明授权
    Deep reinforcement learning for robotic manipulation 有权

    公开(公告)号：US12240113B2

    公开(公告)日：2025-03-04

    申请号：US18526443

    申请日：2023-12-01

    Applicant: GOOGLE LLC

    Inventor： Sergey Levine ,  Ethan Holly ,  Shixiang Gu ,  Timothy Lillicrap

    IPC: G06F17/00 ,  B25J9/16 ,  G05B13/02 ,  G05B19/042 ,  G06N3/008 ,  G06N3/045 ,  G06N3/08

    Abstract: Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.

    DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
    4.
    发明申请
    DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION 有权

    公开(公告)号：US20220388159A1

    公开(公告)日：2022-12-08

    申请号：US17878186

    申请日：2022-08-01

    Applicant: Google LLC

    Inventor： Sergey Levine ,  Ethan Holly ,  Shixiang Gu ,  Timothy Lillicrap

    IPC: B25J9/16 ,  G05B13/02 ,  G06N3/08 ,  G06N3/00 ,  G06N3/04 ,  G05B19/042

    Abstract: Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.

    DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION
    6.
    发明申请
    DEEP REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATION 有权

    公开(公告)号：US20250153352A1

    公开(公告)日：2025-05-15

    申请号：US19025551

    申请日：2025-01-16

    Applicant: GOOGLE LLC

    Inventor： Sergey Levine ,  Ethan Holly ,  Shixiang Gu ,  Timothy Lillicrap

    IPC: B25J9/16 ,  G05B13/02 ,  G05B19/042 ,  G06N3/008 ,  G06N3/045 ,  G06N3/08

    Abstract: Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.

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