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Unsupervised action segmentation from skeleton data just got a whole lot better: a new method significantly outperforms prior work and reduces bias towards short segments.
Hierarchical quantization of robot actions, incorporating both spatial and temporal information, unlocks new state-of-the-art performance in in-context imitation learning.