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A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics
We present a novel hierarchical spatiotemporal action tokenizer for in-context imitation learning. We first propose a hierarchical approach, which consists of two successive levels of vector quantization. In particular, the lower level assigns input actions to fine-grained subclusters, while the hig
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