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    CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT
    2.
    发明申请
    CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT 有权

    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.

    Turn-based reinforcement learning for dialog management
    3.
    发明授权
    Turn-based reinforcement learning for dialog management 有权

    公开(公告)号：US10424302B2

    公开(公告)日：2019-09-24

    申请号：US15782333

    申请日：2017-10-12

    Applicant: Google LLC

    Inventor： Pararth Shah ,  Larry Paul Heck ,  Dilek Hakkani-Tur

    IPC: G10L15/00 ,  G10L15/16 ,  G10L15/30 ,  G10L17/22 ,  H04L12/58 ,  G06F16/9032

    Abstract: Techniques are described related to turn-based reinforcement learning for dialog management. In various implementations, dialog states and corresponding responsive actions generated during a multi-turn human-to-computer dialog session may be obtained. A plurality of turn-level training instances may be generated, each including: a given dialog state of the plurality of dialog states at an outset of a given turn of the human-to-computer dialog session; and a given responsive action that was selected based on the given dialog state. One or more of the turn-level training instances may further include a turn-level feedback value that reflects on the given responsive action selected during the given turn. A reward value may be generated based on an outcome of the human-to-computer dialog session. The dialog management policy model may be trained based on turn-level feedback values of the turn-level training instance(s) and the reward value.

    TURN-BASED REINFORCEMENT LEARNING FOR DIALOG MANAGEMENT
    6.
    发明申请
    TURN-BASED REINFORCEMENT LEARNING FOR DIALOG MANAGEMENT 审中-公开

    公开(公告)号：US20190115027A1

    公开(公告)日：2019-04-18

    申请号：US15782333

    申请日：2017-10-12

    Applicant: Google LLC

    Inventor： Pararth Shah ,  Larry Paul Heck ,  Dilek Hakkani-Tur

    IPC: G10L15/30 ,  G06F17/30 ,  G10L15/16 ,  H04L12/58 ,  G10L17/22 ,  G10L15/00

    CPC classification number: G10L15/30 ,  G06F16/90332 ,  G06F17/27 ,  G10L15/005 ,  G10L15/16 ,  G10L15/22 ,  G10L17/22 ,  H04L51/02

    Abstract: Techniques are described related to turn-based reinforcement learning for dialog management. In various implementations, dialog states and corresponding responsive actions generated during a multi-turn human-to-computer dialog session may be obtained. A plurality of turn-level training instances may be generated, each including: a given dialog state of the plurality of dialog states at an outset of a given turn of the human-to-computer dialog session; and a given responsive action that was selected based on the given dialog state. One or more of the turn-level training instances may further include a turn-level feedback value that reflects on the given responsive action selected during the given turn. A reward value may be generated based on an outcome of the human-to-computer dialog session. The dialog management policy model may be trained based on turn-level feedback values of the turn-level training instance(s) and the reward value.

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