公开(公告)号：US20210237266A1

公开(公告)日：2021-08-05

申请号：US17052679

申请日：2019-06-14

Applicant: Google LLC

Inventor： Dmitry Kalashnikov ,  Alexander Irpan ,  Peter Pastor Sampedro ,  Julian Ibarz ,  Alexander Herzog ,  Eric Jang ,  Deirdre Quillen ,  Ethan Holly ,  Sergey Levine

IPC: B25J9/16 ,  G06N3/08

Abstract: Using large-scale reinforcement learning to train a policy model that can be utilized by a robot in performing a robotic task in which the robot interacts with one or more environmental objects. In various implementations, off-policy deep reinforcement learning is used to train the policy model, and the off-policy deep reinforcement learning is based on self-supervised data collection. The policy model can be a neural network model. Implementations of the reinforcement learning utilized in training the neural network model utilize a continuous-action variant of Q-learning. Through techniques disclosed herein, implementations can learn policies that generalize effectively to previously unseen objects, previously unseen environments, etc.

公开(公告)号：US11045949B2

公开(公告)日：2021-06-29

申请号：US16823947

申请日：2020-03-19

Applicant: Google LLC

Inventor： Sudheendra Vijayanarasimhan ,  Eric Jang ,  Peter Pastor Sampedro ,  Sergey Levine

IPC: G05B19/04 ,  G05B19/18 ,  B25J9/16 ,  G05B13/02 ,  G06N3/04 ,  G06N3/08 ,  G06N3/00

Abstract: Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).