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Addressing the challenge of organizing scattered items in domestic spaces iscomplicated by the diversity and subjective nature of tidiness. Just as thecomplexity of human language allows for multiple expressions of the same idea,household tidiness preferences and organizational patterns vary widely, sopresetting object locations would limit the adaptability to new objects andenvironments. Inspired by advancements in natural language processing (NLP),this paper introduces a self-supervised learning framework that allows robotsto understand and replicate the concept of tidiness from demonstrations ofwell-organized layouts, akin to using conversational datasets to train LargeLanguage Models(LLM). We leverage a transformer neural network to predict theplacement of subsequent objects. We demonstrate a ``knolling'' system with arobotic arm and an RGB camera to organize items of varying sizes and quantitieson a table. Our method not only trains a generalizable concept of tidiness,enabling the model to provide diverse solutions and adapt to different numbersof objects, but it can also incorporate human preferences to generatecustomized tidy tables without explicit target positions for each object.

Knolling Bot: Learning Robotic Object Arrangement from Tidy Demonstrations