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ATTENTION: The works hosted here are being migrated to a new repository that will consolidate resources, improve discoverability, and better show UTA's research impact on the global community. We will update authors as the migration progresses. Please see MavMatrix for more information.
In this paper, we present a novel method to learn end-to-end visuomotor policies for robotic manipulators. The method computes
state-action mappings in a supervised learning manner from video
demonstrations and robot ...