首页|Study Findings on Robotics and Automation Are Outlined in Reports from University of Texas Austin (Programmatic Imitation Learning From Unlabeled and Noisy Demo nstrations)
Study Findings on Robotics and Automation Are Outlined in Reports from University of Texas Austin (Programmatic Imitation Learning From Unlabeled and Noisy Demo nstrations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting out of Austin, Texas, by NewsRx editors, research stated, “Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations . Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several wellknown limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to refine and adapt .”
AustinTexasUnited StatesNorth and Central AmericaRobotics and AutomationRoboticsUniversity of Texas Austin
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