公开(公告)号：US20240118667A1

公开(公告)日：2024-04-11

申请号：US17767675

申请日：2020-05-15

Applicant: GOOGLE LLC

Inventor： Kanishka Rao ,  Chris Harris ,  Julian Ibarz ,  Alexander Irpan ,  Seyed Mohammad Khansari Zadeh ,  Sergey Levine

IPC: G05B13/02 ,  B25J9/16 ,  B25J19/02

CPC classification number: G05B13/0265 ,  B25J9/1605 ,  B25J9/163 ,  B25J9/1697 ,  B25J19/023

Abstract: Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model (“Sim2Real” model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning (“RL”) neural network model (RL-network), such as an RL-network that represents a Q-function.

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

IPC: B25J9/16 ,  B25J13/06

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.

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

IPC: B25J9/16 ,  B25J13/06

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.

公开(公告)号：US20250058475A1

公开(公告)日：2025-02-20

申请号：US18936720

申请日：2024-11-04

Applicant: GOOGLE LLC

Inventor： Soeren Pirk ,  Seyed Mohammad Khansari Zadeh ,  Karol Hausman ,  Alexander Toshev

IPC: B25J9/16 ,  G06N3/045 ,  G06V10/147 ,  G06V10/44 ,  G06V10/82 ,  G06V20/10 ,  G06V20/13 ,  G06V20/17

Abstract: Training and/or using a machine learning model for performing robotic tasks is disclosed herein. In many implementations, an environment-conditioned action sequence prediction model is used to determine a set of actions as well as a corresponding particular order for the actions for the robot to perform to complete the task. In many implementations, each action in the set of actions has a corresponding action network used to control the robot in performing the action.

公开(公告)号：US12134199B2

公开(公告)日：2024-11-05

申请号：US17642325

申请日：2020-09-09

Applicant: GOOGLE LLC

Inventor： Soeren Pirk ,  Seyed Mohammad Khansari Zadeh ,  Karol Hausman ,  Alexander Toshev

IPC: B25J9/16 ,  G06N3/045 ,  G06V10/147 ,  G06V10/44 ,  G06V10/82 ,  G06V20/10 ,  G06V20/17 ,  G06V20/13

Abstract: Training and/or using a machine learning model for performing robotic tasks is disclosed herein. In many implementations, an environment-conditioned action sequence prediction model is used to determine a set of actions as well as a corresponding particular order for the actions for the robot to perform to complete the task. In many implementations, each action in the set of actions has a corresponding action network used to control the robot in performing the action.

公开(公告)号：US12112494B2

公开(公告)日：2024-10-08

申请号：US17053335

申请日：2020-02-28

Applicant: Google LLC

Inventor： Honglak Lee ,  Xinchen Yan ,  Soeren Pirk ,  Yunfei Bai ,  Seyed Mohammad Khansari Zadeh ,  Yuanzheng Gong ,  Jasmine Hsu

IPC: G06T7/55 ,  B25J9/16 ,  B25J13/08 ,  G06F18/21 ,  G06T7/50 ,  G06V20/10 ,  G06V20/64

CPC classification number: G06T7/55 ,  B25J9/1605 ,  B25J9/163 ,  B25J9/1669 ,  B25J9/1697 ,  B25J13/08 ,  G06F18/2163 ,  G06T7/50 ,  G06V20/10 ,  G06V20/64 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132

Abstract: Implementations relate to training a point cloud prediction model that can be utilized to process a single-view two-and-a-half-dimensional (2.5D) observation of an object, to generate a domain-invariant three-dimensional (3D) representation of the object. Implementations additionally or alternatively relate to utilizing the domain-invariant 3D representation to train a robotic manipulation policy model using, as at least part of the input to the robotic manipulation policy model during training, the domain-invariant 3D representations of simulated objects to be manipulated. Implementations additionally or alternatively relate to utilizing the trained robotic manipulation policy model in control of a robot based on output generated by processing generated domain-invariant 3D representations utilizing the robotic manipulation policy model.

公开(公告)号：US11887363B2

公开(公告)日：2024-01-30

申请号：US17279924

申请日：2019-09-27

Applicant: Google LLC

Inventor： Soeren Pirk ,  Yunfei Bai ,  Pierre Sermanet ,  Seyed Mohammad Khansari Zadeh ,  Harrison Lynch

IPC: G06V20/10 ,  B25J9/16 ,  B25J13/00 ,  G05B13/02 ,  G06N3/08 ,  G10L15/22 ,  G06F18/21 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/70 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06V20/10 ,  B25J9/1697 ,  B25J13/003 ,  G05B13/027 ,  G06F18/217 ,  G06F18/2413 ,  G06N3/08 ,  G06V10/764 ,  G06V10/768 ,  G06V10/776 ,  G06V10/82 ,  G10L15/22

Abstract: Training a machine learning model (e.g., a neural network model such as a convolutional neural network (CNN) model) so that, when trained, the model can be utilized in processing vision data (e.g., from a vision component of a robot), that captures an object, to generate a rich object-centric embedding for the vision data. The generated embedding can enable differentiation of even subtle variations of attributes of the object captured by the vision data.

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.

公开(公告)号：US20220076099A1

公开(公告)日：2022-03-10

申请号：US17432366

申请日：2020-02-19

Applicant: Google LLC

Inventor： Pierre Sermanet ,  Seyed Mohammad Khansari Zadeh ,  Harrison Corey Lynch

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes controlling the agent using a policy neural network that processes a policy input that includes (i) a current observation, (ii) a goal observation, and (iii) a selected latent plan to generate a current action output that defines an action to be performed in response to the current observation.