Amortized Object and Scene Perception for Long-term Robot Manipulation
Ferenc Balint-Benczedi,
Michael Beetz
Abstract:Mobile robots, performing long-term manipulation activities in human environments, have to perceive a wide variety of objects possessing very different visual characteristics and need to reliably keep track of these throughout the execution of a task. In order to be efficient, robot perception capabilities need to go beyond what is currently perceivable and should be able to answer queries about both current and past scenes. In this paper we investigate a perception system for long-term robot manipulation that… Show more
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