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：US20210101286A1

公开(公告)日：2021-04-08

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: B25J9/16 ,  G06K9/00 ,  G06K9/62 ,  G06T7/50 ,  B25J13/08

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：US11554483B2

公开(公告)日：2023-01-17

申请号：US17094111

申请日：2020-11-10

Applicant: Google LLC

Inventor： James Davidson ,  Xinchen Yan ,  Yunfei Bai ,  Honglak Lee ,  Abhinav Gupta ,  Seyed Mohammad Khansari Zadeh ,  Arkanath Pathak ,  Jasmine Hsu

IPC: B25J9/16

Abstract: Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.

公开(公告)号：US20210053217A1

公开(公告)日：2021-02-25

申请号：US17094111

申请日：2020-11-10

Applicant: Google LLC

Inventor： James Davidson ,  Xinchen Yan ,  Yunfei Bai ,  Honglak Lee ,  Abhinav Gupta ,  Seyed Mohammad Khansari Zadeh ,  Arkanath Pathak ,  Jasmine Hsu

IPC: B25J9/16

Abstract: Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.

公开(公告)号：US20200094405A1

公开(公告)日：2020-03-26

申请号：US16617169

申请日：2018-06-18

Applicant: Google LLC

Inventor： James Davidson ,  Xinchen Yan ,  Yunfei Bai ,  Honglak Lee ,  Abhinav Gupta ,  Seyed Mohammad Khansari Zadeh ,  Arkanath Pathak ,  Jasmine Hsu

IPC: B25J9/16

Abstract: Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.