Patents by Inventor Peter Pastor Sampedro

Peter Pastor Sampedro has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 9914213
    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 semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).
    Type: Grant
    Filed: March 2, 2017
    Date of Patent: March 13, 2018
    Assignee: GOOGLE LLC
    Inventors: Sudheendra Vijayanarasimhan, Eric Jang, Peter Pastor Sampedro, Sergey Levine
  • Publication number: 20170252924
    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 semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).
    Type: Application
    Filed: March 2, 2017
    Publication date: September 7, 2017
    Inventors: Sudheendra Vijayanarasimhan, Eric Jang, Peter Pastor Sampedro, Sergey Levine
  • Publication number: 20170252922
    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.
    Type: Application
    Filed: December 13, 2016
    Publication date: September 7, 2017
    Inventors: Sergey Levine, Peter Pastor Sampedro, Alex Krizhevsky