Knolling bot 2.0: Enhancing Object Organization with Self-supervised Graspability Estimation
CoRR(2023)
Abstract
Building on recent advancements in transformer based approaches for domestic
robots performing knolling, the art of organizing scattered items into neat
arrangements. This paper introduces Knolling bot 2.0. Recognizing the
challenges posed by piles of objects or items situated closely together, this
upgraded system incorporates a self-supervised graspability estimation model.
If objects are deemed ungraspable, an additional behavior will be executed to
separate the objects before knolling the table. By integrating this grasp
prediction mechanism with existing visual perception and transformer based
knolling models, an advanced system capable of decluttering and organizing even
more complex and densely populated table settings is demonstrated. Experimental
evaluations demonstrate the effectiveness of this module, yielding a
graspability prediction accuracy of 95.7%.
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Key words
object organization,bot,self-supervised
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