Patents by Inventor Nicolas Sievers

Nicolas Sievers 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).

  • SYSTEM(S) AND METHOD(S) OF USING IMITATION LEARNING IN TRAINING AND REFINING ROBOTIC CONTROL POLICIES
    Publication number: 20230381970
    Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.
    Type: Application
    Filed: August 11, 2023
    Publication date: November 30, 2023
    Inventors: Seyed Mohammad Khansari Zadeh, Eric Jang, Daniel Lam, Daniel Kappler, Matthew Bennice, Brent Austin, Yunfei Bai, Sergey Levine, Alexander Irpan, Nicolas Sievers, Chelsea Finn
  • System(s) and method(s) of using imitation learning in training and refining robotic control policies
    Patent number: 11772272
    Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.
    Type: Grant
    Filed: March 16, 2021
    Date of Patent: October 3, 2023
    Assignee: GOOGLE LLC
    Inventors: Seyed Mohammad Khansari Zadeh, Eric Jang, Daniel Lam, Daniel Kappler, Matthew Bennice, Brent Austin, Yunfei Bai, Sergey Levine, Alexander Irpan, Nicolas Sievers, Chelsea Finn
  • SYSTEM(S) AND METHOD(S) OF USING IMITATION LEARNING IN TRAINING AND REFINING ROBOTIC CONTROL POLICIES
    Publication number: 20220297303
    Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.
    Type: Application
    Filed: March 16, 2021
    Publication date: September 22, 2022
    Inventors: Seyed Mohammad Khansari Zadeh, Eric Jang, Daniel Lam, Daniel Kappler, Matthew Bennice, Brent Austin, Yunfei Bai, Sergey Levine, Alexander Irpan, Nicolas Sievers, Chelsea Finn