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.