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公开(公告)号：US20250153363A1
公开(公告)日：2025-05-15
申请号：US19025627
申请日：2025-01-16
Applicant: GOOGLE LLC
Inventor： Seyed Mohammad Khansari Zadeh , Eric Jang , Daniel Lam , Daniel Kappler , Matthew Bennice , Brent Austin , Yunfei Bai , Sergey Levine , Alexander Irpan , Nicolas Sievers , Chelsea Finn
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.
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公开(公告)号：US20220105624A1
公开(公告)日：2022-04-07
申请号：US17422260
申请日：2020-01-23
Applicant: Google LLC
Inventor： Mrinal Kalakrishnan , Yunfei Bai , Paul Wohlhart , Eric Jang , Chelsea Finn , Seyed Mohammad Khansari Zadeh , Sergey Levine , Allan Zhou , Alexander Herzog , Daniel Kappler
IPC: B25J9/16
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.
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Patent Agency Ranking
公开(公告)号：US12083678B2
公开(公告)日：2024-09-10
申请号：US17422260
申请日：2020-01-23
Applicant: Google LLC
Inventor： Mrinal Kalakrishnan , Yunfei Bai , Paul Wohlhart , Eric Jang , Chelsea Finn , Seyed Mohammad Khansari Zadeh , Sergey Levine , Allan Zhou , Alexander Herzog , Daniel Kappler
IPC: B25J9/16
CPC classification number: B25J9/163 , G05B2219/40116 , G05B2219/40499
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.
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公开(公告)号：US20220063089A1
公开(公告)日：2022-03-03
申请号：US17524185
申请日：2021-11-11
Applicant: GOOGLE LLC
Inventor： Sergey Levine , Chelsea Finn , Ian Goodfellow
Abstract: Some implementations of this specification are directed generally to deep machine learning methods and apparatus related to predicting motion(s) (if any) that will occur to object(s) in an environment of a robot in response to particular movement of the robot in the environment. Some implementations are directed to training a deep neural network model to predict at least one transformation (if any), of an image of a robot's environment, that will occur as a result of implementing at least a portion of a particular movement of the robot in the environment. The trained deep neural network model may predict the transformation based on input that includes the image and a group of robot movement parameters that define the portion of the particular movement.
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公开(公告)号：US12226920B2
公开(公告)日：2025-02-18
申请号：US18233251
申请日：2023-08-11
Applicant: GOOGLE LLC
Inventor： Seyed Mohammad Khansari Zadeh , Eric Jang , Daniel Lam , Daniel Kappler , Matthew Bennice , Brent Austin , Yunfei Bai , Sergey Levine , Alexander Irpan , Nicolas Sievers , Chelsea Finn
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.
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6.发明公开公开(公告)号：US20230381970A1
公开(公告)日：2023-11-30
申请号：US18233251
申请日：2023-08-11
Applicant: GOOGLE LLC
Inventor： Seyed Mohammad Khansari Zadeh , Eric Jang , Daniel Lam , Daniel Kappler , Matthew Bennice , Brent Austin , Yunfei Bai , Sergey Levine , Alexander Irpan , Nicolas Sievers , Chelsea Finn
CPC classification number: B25J9/1697 , B25J9/163 , B25J9/1661 , B25J9/161 , B25J13/06
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.
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公开(公告)号：US11772272B2
公开(公告)日：2023-10-03
申请号：US17203296
申请日：2021-03-16
Applicant: GOOGLE LLC
Inventor： Seyed Mohammad Khansari Zadeh , Eric Jang , Daniel Lam , Daniel Kappler , Matthew Bennice , Brent Austin , Yunfei Bai , Sergey Levine , Alexander Irpan , Nicolas Sievers , Chelsea Finn
CPC classification number: B25J9/1697 , B25J9/161 , B25J9/163 , B25J9/1661 , B25J13/06
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.
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