Deep machine learning methods and apparatus for robotic grasping
US10207402B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 13, 2016 |
| Grant date | Feb 19, 2019 |
| Priority date | — |
| Expiry date | Jun 21, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/39509
- WIPO fieldHandling
- WIPO sectorMechanical engineering
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 deep neural network to predict a measure that candidate motion data for an end effector of a robot will result in a successful grasp of one or more objects by the end effector. Some implementations are directed to utilization of the trained deep neural network to servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector. For example, the trained deep neural network may be utilized in the iterative updating of motion control commands for one or more actuators of a robot that control the pose of a grasping end effector of the robot, and to determine when to generate grasping control commands to effectuate an attempted grasp by the grasping end effector.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.