<ul><li>Researchers propose a pipeline named JUICER to improve imitation learning performance in robotic assembly tasks.</li><li>The pipeline combines diffusion policy architectures, data noising, and iterative model development cycles.</li><li>JUICER demonstrates significant improvements in overall task success compared to baseline methods.</li><li>The provided tools and datasets empower the research community to explore advancements in robotic learning for assembly tasks.</li></ul>

This AI Paper Proposes a Pipeline for Improving Imitation Learning Performance with a Small Human Demonstration Budget

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