The paper introduces a self-supervised learning framework inspired by natural language processing to teach robots the concept of tidiness. By leveraging transformer neural networks, robots can predict object placements based on well-organized layouts without presetting object locations. The study focuses on the challenges of organizing diverse objects in household settings and the need for task-agnostic planners. The research aims to imbue robots with human-like cognition for adaptable organization beyond specific tasks. The proposed knolling system decouples cognitive models from visual perception and motor control, enhancing modularity. By using a Gaussian Mixture Model, the model addresses multi-label prediction challenges inherent in knolling tasks.
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by Yuhang Hu,Zh... at arxiv.org 03-19-2024
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