公开(公告)号：US10773382B2

公开(公告)日：2020-09-15

申请号：US15913212

申请日：2018-03-06

Applicant: X Development LLC

Inventor： Yunfei Bai ,  Kuan Fang ,  Stefan Hinterstoisser ,  Mrinal Kalakrishnan

IPC: B25J9/16

Abstract: Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. At least portions of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular task—and the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.

公开(公告)号：US20190084151A1

公开(公告)日：2019-03-21

申请号：US15913212

申请日：2018-03-06

Applicant: X Development LLC

Inventor： Yunfei Bai ,  Kuan Fang ,  Stefan Hinterstoisser ,  Mrinal Kalakrishnan

IPC: B25J9/16

Abstract: Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. At least portions of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular task—and the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.