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    Robotic control using value distributions
    1.
    发明授权
    Robotic control using value distributions 有权

    公开(公告)号：US11571809B1

    公开(公告)日：2023-02-07

    申请号：US17017920

    申请日：2020-09-11

    Applicant: X Development LLC

    Inventor： Cristian Bodnar ,  Adrian Li ,  Karol Hausman ,  Peter Pastor Sampedro ,  Mrinal Kalakrishnan

    IPC: B25J9/16 ,  B25J13/08

    Abstract: Techniques are described herein for robotic control using value distributions. In various implementations, as part of performing a robotic task, state data associated with the robot in an environment may be generated based at least in part on vision data captured by a vision component of the robot. A plurality of candidate actions may be sampled, e.g., from continuous action space. A trained critic neural network model that represents a learned value function may be used to process a plurality of state-action pairs to generate a corresponding plurality of value distributions. Each state-action pair may include the state data and one of the plurality of sampled candidate actions. The state-action pair corresponding to the value distribution that satisfies one or more criteria may be selected from the plurality of state-action pairs. The robot may then be controlled to implement the sampled candidate action of the selected state-action pair.

    Action prediction networks for robotic grasping
    2.
    发明授权
    Action prediction networks for robotic grasping 有权

    公开(公告)号：US11325252B2

    公开(公告)日：2022-05-10

    申请号：US16570522

    申请日：2019-09-13

    Applicant: X Development LLC

    Inventor： Adrian Li ,  Peter Pastor Sampedro ,  Mengyuan Yan ,  Mrinal Kalakrishnan

    IPC: B25J9/00 ,  B25J9/16

    Abstract: Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.

    ACTION PREDICTION NETWORKS FOR ROBOTIC GRASPING
    3.
    发明申请
    ACTION PREDICTION NETWORKS FOR ROBOTIC GRASPING 审中-公开

    公开(公告)号：US20200086483A1

    公开(公告)日：2020-03-19

    申请号：US16570522

    申请日：2019-09-13

    Applicant: X Development LLC

    Inventor： Adrian Li ,  Peter Pastor Sampedro ,  Mengyuan Yan ,  Mrinal Kalakrishnan

    IPC: B25J9/16

    Abstract: Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.

    Real time robot collision avoidance
    4.
    发明授权
    Real time robot collision avoidance 有权

    公开(公告)号：US10131053B1

    公开(公告)日：2018-11-20

    申请号：US15265547

    申请日：2016-09-14

    Applicant: X Development LLC

    Inventor： Peter Pastor Sampedro ,  Umashankar Nagarajan

    IPC: B25J9/00 ,  B25J9/16

    Abstract: Methods and apparatus related to robot collision avoidance. One method may include: receiving robot instructions to be performed by a robot; at each of a plurality of control cycles of processor(s) of the robot: receiving trajectories to be implemented by actuators of the robot, wherein the trajectories define motion states for the actuators of the robot during the control cycle or a next control cycle, and wherein the trajectories are generated based on the robot instructions; determining, based on a current motion state of the actuators and the trajectories to be implemented, whether implementation of the trajectories by the actuators prevents any collision avoidance trajectory from being achieved; and selectively providing the trajectories or collision avoidance trajectories for operating the actuators of the robot during the control cycle or the next control cycle depending on a result of the determining.

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