Robotic systems using learning to provide real-time vibration-suppressing control
US11701774B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 16, 2020 |
| Grant date | Jul 18, 2023 |
| Priority date | — |
| Expiry date | Oct 22, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/39271
- WIPO fieldHandling
- WIPO sectorMechanical engineering
Abstract
A robot control method, and associated robot controllers and robots operating with such methods and controllers, providing real-time vibration suppression. The control method involves learning to support real-time, vibration-suppressing control. The method uses state-of-the-art machine learning techniques in conjunction with a differentiable dynamics simulator to yield fast and accurate vibration suppression. Vibration suppression using offline simulation approaches that can be computationally expensive may be used to create training data for the controller, which may be provide by a variety of neural network configurations. In other cases, sensory feedback from sensors onboard the robot being controlled can be used to provide training data to account for wear of the robot's components.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.