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In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solveunseen tasks with few expert demonstrations becomes a popular researchdirection. The following capabilities are essential in robotics applications:(1) Behaving in compound tasks that contain multiple stages. (2) Retrievingknowledge from few length-variant and misalignment demonstrations. (3) Learningfrom a different expert. No previous work can achieve these abilities at thesame time. In this work, we conduct FSIL problem under the union of abovesettings and introduce a novel stage conscious attention network (SCAN) toretrieve knowledge from few demonstrations simultaneously. SCAN uses anattention module to identify each stage in length-variant demonstrations.Moreover, it is designed under demonstration-conditioned policy that learns therelationship between experts and agents. Experiment results show that SCAN canlearn from different experts without fine-tuning and outperform baselines incomplicated compound tasks with explainable visualization.

Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation