公开(公告)号：US20230330846A1

公开(公告)日：2023-10-19

申请号：US18028966

申请日：2021-10-01

Applicant: DeepMind Technologies Limited

Inventor： Yuxiang Zhou ,  Yusuf Aytar ,  Konstantinos Bousmalis

IPC: B25J9/16

CPC classification number: B25J9/163

Abstract: It is described a system implemented as computer programs on one or more computers in one or more locations that trains a policy neural network that is used to control a robot, i.e., to select actions to be performed by the robot while the robot is interacting with an environment, through imitation learning in order to cause the robot to perform particular tasks in the environment.

公开(公告)号：US20210103815A1

公开(公告)日：2021-04-08

申请号：US17065489

申请日：2020-10-07

Applicant: DeepMind Technologies Limited

Inventor： Rae Chan Jeong ,  Yusuf Aytar ,  David Khosid ,  Yuxiang Zhou ,  Jacqueline Ok-chan Kay ,  Thomas Lampe ,  Konstantinos Bousmalis ,  Francesco Nori

IPC: G06N3/08 ,  G05B19/4155

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network for use in controlling a real-world agent in a real-world environment. One of the methods includes training the policy neural network by optimizing a first task-specific objective that measures a performance of the policy neural network in controlling a simulated version of the real-world agent; and then training the policy neural network by jointly optimizing (i) a self-supervised objective that measures at least a performance of internal representations generated by the policy neural network on a self-supervised task performed on real-world data and (ii) a second task-specific objective that measures the performance of the policy neural network in controlling the simulated version of the real-world agent.