Learning robotic tasks using one or more neural networks
US11941719B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 23, 2019 |
| Grant date | Mar 26, 2024 |
| Priority date | — |
| Expiry date | Jan 19, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.