中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

4 records (took 0.02 seconds)

    UPDATE OF LOCAL FEATURES MODEL BASED ON CORRECTION TO ROBOT ACTION
    1.
    发明申请
    UPDATE OF LOCAL FEATURES MODEL BASED ON CORRECTION TO ROBOT ACTION 审中-公开

    公开(公告)号：WO2019010137A1

    公开(公告)日：2019-01-10

    申请号：PCT/US2018/040644

    申请日：2018-07-02

    Applicant: X DEVELOPMENT LLC

    Inventor： SHANKAR, Krishna ,  HUDSON, Nicolas ,  TOSHEV, Alexander

    IPC: G06N3/00 ,  G06K9/62 ,  G06N3/08

    Abstract: Methods, apparatus, and computer-readable media for determining and utilizing corrections to robot actions. Some implementations are directed to updating a local features model of a robot in response to determining a human correction of an action performed by the robot. The local features model is used to determine, based on an embedding generated over a corresponding neural network model, one or more features that are most similar to the generated embedding. Updating the local features model in response to a human correction can include updating a feature embedding, of the local features model, that corresponds to the human correction. Adjustment(s) to the features model can immediately improve robot performance without necessitating retraining of the corresponding neural network model.

    SHARING LEARNED INFORMATION AMONG ROBOTS
    3.
    发明申请
    SHARING LEARNED INFORMATION AMONG ROBOTS 审中-公开

    公开(公告)号：WO2019133081A1

    公开(公告)日：2019-07-04

    申请号：PCT/US2018/050868

    申请日：2018-09-13

    Applicant: X DEVELOPMENT LLC

    Inventor： RAJKUMAR, Nareshkumar ,  LEGER, Patrick ,  HUDSON, Nicolas ,  SHANKAR, Krishna ,  HESSMER, Rainer

    IPC: B25J9/00 ,  B25J9/16

    CPC classification number: G06N3/08 ,  B25J9/0003 ,  B25J9/1671 ,  G05B2219/45108 ,  H04L67/12 ,  H04L67/2842

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.

Patent Agency Ranking