Patents by Inventor Leonardo Ruggiero Bachega

Leonardo Ruggiero Bachega 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: 11584008
    Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
    Type: Grant
    Filed: October 9, 2020
    Date of Patent: February 21, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Brian C. Beckman, Leonardo Ruggiero Bachega, Brandon William Porter, Benjamin Lev Snyder, Michael Vogelsong, Corrinne Yu
  • Patent number: 10800040
    Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
    Type: Grant
    Filed: December 14, 2017
    Date of Patent: October 13, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Brian C. Beckman, Leonardo Ruggiero Bachega, Brandon William Porter, Benjamin Lev Snyder, Michael Vogelsong, Corrinne Yu
  • Patent number: 10792810
    Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
    Type: Grant
    Filed: December 14, 2017
    Date of Patent: October 6, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Brian C. Beckman, Leonardo Ruggiero Bachega, Brandon William Porter, Benjamin Lev Snyder, Michael Vogelsong, Corrinne Yu
  • Patent number: 10766136
    Abstract: A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.
    Type: Grant
    Filed: November 3, 2017
    Date of Patent: September 8, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Brandon William Porter, Leonardo Ruggiero Bachega, Brian C. Beckman, Benjamin Lev Snyder, Michael Vogelsong, Corrinne Yu
  • Patent number: 10766137
    Abstract: A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.
    Type: Grant
    Filed: November 3, 2017
    Date of Patent: September 8, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Brandon William Porter, Leonardo Ruggiero Bachega, Brian C. Beckman, Benjamin Lev Snyder, Michael Vogelsong, Corrinne Yu