Patents by Inventor Allan Zhou

Allan Zhou 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).

  • Efficient adaption of robot control policy for new task using meta-learning based on meta-imitation learning and meta-reinforcement learning
    Patent number: 12083678
    Abstract: Techniques are disclosed that enable training a meta-learning model, for use in causing a robot to perform a task, using imitation learning as well as reinforcement learning. Some implementations relate to training the meta-learning model using imitation learning based on one or more human guided demonstrations of the task. Additional or alternative implementations relate to training the meta-learning model using reinforcement learning based on trials of the robot attempting to perform the task. Further implementations relate to using the trained meta-learning model to few shot (or one shot) learn a new task based on a human guided demonstration of the new task.
    Type: Grant
    Filed: January 23, 2020
    Date of Patent: September 10, 2024
    Assignee: GOOGLE LLC
    Inventors: Mrinal Kalakrishnan, Yunfei Bai, Paul Wohlhart, Eric Jang, Chelsea Finn, Seyed Mohammad Khansari Zadeh, Sergey Levine, Allan Zhou, Alexander Herzog, Daniel Kappler
  • EFFICIENT ADAPTION OF ROBOT CONTROL POLICY FOR NEW TASK USING META-LEARNING BASED ON META-IMITATION LEARNING AND META-REINFORCEMENT LEARNING
    Publication number: 20220105624
    Abstract: Techniques are disclosed that enable training a meta-learning model, for use in causing a robot to perform a task, using imitation learning as well as reinforcement learning. Some implementations relate to training the meta-learning model using imitation learning based on one or more human guided demonstrations of the task. Additional or alternative implementations relate to training the meta-learning model using reinforcement learning based on trials of the robot attempting to perform the task. Further implementations relate to using the trained meta-learning model to few shot (or one shot) learn a new task based on a human guided demonstration of the new task.
    Type: Application
    Filed: January 23, 2020
    Publication date: April 7, 2022
    Inventors: Mrinal Kalakrishnan, Yunfei Bai, Paul Wohlhart, Eric Jang, Chelsea Finn, Seyed Mohammad Khansari Zadeh, Sergey Levine, Allan Zhou, Alexander Herzog, Daniel Kappler