Invention Grant
- Patent Title: Artificial intelligence system for learning robotic control policies
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Application No.: US15842707Application Date: 2017-12-14
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Publication No.: US10792810B1Publication Date: 2020-10-06
- Inventor: Brian C. Beckman , Leonardo Ruggiero Bachega , Brandon William Porter , Benjamin Lev Snyder , Michael Vogelsong , Corrinne Yu
- Applicant: Amazon Technologies, Inc.
- Applicant Address: US WA Seattle
- Assignee: Amazon Technologies, Inc.
- Current Assignee: Amazon Technologies, Inc.
- Current Assignee Address: US WA Seattle
- Agency: Knobbe, Martens, Olson & Bear, LLP
- Main IPC: G06F17/00
- IPC: G06F17/00 ; B25J9/16

Abstract:
A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
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