Simulation of tasks using neural networks
US12275146B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 1, 2019 |
| Grant date | Apr 15, 2025 |
| Priority date | — |
| Expiry date | Jun 24, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05D2101/15
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.