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Found 2 papers, 1 papers with code
Decision Transformer under Random Frame Dropping
Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world.
Extraneousness-Aware Imitation Learning
To tackle this problem, we introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised approach that learns visuomotor policies from third-person demonstrations with extraneous subsequences.
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