Learning from operator data for practical autonomy

Machine learning, evaluating, and reinforced learning within systems or apparatuses enables autonomy to a complexity level beyond automation. Inferences are made using machine learning based on observations, images, or video feed of operator input. The inferences are evaluated or classified and mane...

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Bibliographic Details
Main Authors McLean, Angus L, Bertram, Joshua R
Format Patent
LanguageEnglish
Published 02.03.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONTROL OR REGULATING SYSTEMS IN GENERAL
CONTROLLING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
FUNCTIONAL ELEMENTS OF SUCH SYSTEMS
GYROSCOPIC INSTRUMENTS
MEASURING
MEASURING DISTANCES, LEVELS OR BEARINGS
MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS
NAVIGATION
PHOTOGRAMMETRY OR VIDEOGRAMMETRY
PHYSICS
REGULATING
SURVEYING
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
TESTING
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Summary:Machine learning, evaluating, and reinforced learning within systems or apparatuses enables autonomy to a complexity level beyond automation. Inferences are made using machine learning based on observations, images, or video feed of operator input. The inferences are evaluated or classified and maneuvers are performed based on the evaluating or the classification. The performed maneuvers may be further evaluated for scoring or weighting. The reinforcement learning may perform updates based on the scoring, weighting, and a maximizing reward function such that the machine learning is constantly improving.
Bibliography:Application Number: US201715629555