Domain adaptation for robotic control using self-supervised learning
US12353993B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 7, 2020 |
| Grant date | Jul 8, 2025 |
| Priority date | — |
| Expiry date | Mar 14, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network for use in controlling a real-world agent in a real-world environment. One of the methods includes training the policy neural network by optimizing a first task-specific objective that measures a performance of the policy neural network in controlling a simulated version of the real-world agent; and then training the policy neural network by jointly optimizing (i) a self-supervised objective that measures at least a performance of internal representations generated by the policy neural network on a self-supervised task performed on real-world data and (ii) a second task-specific objective that measures the performance of the policy neural network in controlling the simulated version of the real-world agent.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.