Reinforcement learning for human robot interaction
US11494641B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 27, 2017 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Feb 5, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A system and method of teaching a neural network through reinforcement learning methodology. The system includes a machine-readable medium having one or more processors that perform a motion task to produce a first result corresponding to navigating a device during a first episode and performing an interaction task during that same episode. After completion of the first episode a processor calculates a Q value change based on the first task result and the second task result. The processor then modifies parameters based on the Q value change such that during subsequent episode iterations the motion task and interactive task are improved and a smooth and continuous transition occurs between these two tasks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.