Simulation-real world feedback loop for learning robotic control policies
US11584008B1 · kind B1 · utility
2Cited by
4References
19Claims
0Family size
Assignee
Inventors
Key dates
| Filing date | Oct 9, 2020 |
| Grant date | Feb 21, 2023 |
| Priority date | — |
| Expiry date | Mar 11, 2041 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB25J9/1697
- WIPO fieldHandling
- WIPO sectorMechanical engineering
Abstract
A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.