Deep reinforcement learning for robotic manipulation
US11897133B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 1, 2022 |
| Grant date | Feb 13, 2024 |
| Priority date | — |
| Expiry date | Aug 1, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/40499
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.