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We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training.
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Learning dense visual correspondences in simulation to smooth and fold real fabrics
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Bc-z: Zero-shot task generalization with robotic imitation learning
E. Jang et al · 2021
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Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
E. Johns · 2021
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Coarse-to-fine for sim-to-real: Sub-millimetre precision across wide task spaces
E. Valassakis et al · 2021
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You only demonstrate once: Category-level manipulation from single visual demonstration
B. Wen et al · 2022
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