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Building on recent advancements in transformer based approaches for domesticrobots performing knolling, the art of organizing scattered items into neatarrangements. This paper introduces Knolling bot 2.0. Recognizing thechallenges posed by piles of objects or items situated closely together, thisupgraded system incorporates a self-supervised graspability estimation model.If objects are deemed ungraspable, an additional behavior will be executed toseparate the objects before knolling the table. By integrating this graspprediction mechanism with existing visual perception and transformer basedknolling models, an advanced system capable of decluttering and organizing evenmore complex and densely populated table settings is demonstrated. Experimentalevaluations demonstrate the effectiveness of this module, yielding agraspability prediction accuracy of 95.7%.

Knolling bot 2.0: Enhancing Object Organization with Self-supervised Graspability Estimation