A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics

Abstract: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 higher level further maps fine-grained subclusters to clusters. Our hierarchical approach outperforms the non-hierarchical counterpart, while mainly exploiting spatial information by reconstructing input actions. Furthermore, we extend our approach by utilizing both spatial and temporal cues, forming a hierarchical spatiotemporal action tokenizer, namely HiST-AT. Specifically, our hierarchical spatiotemporal approach conducts multi-level clustering, while simultaneously recovering input actions and their associated timestamps. Finally, extensive evaluations on multiple simulation and real robotic manipulation benchmarks show that our approach establishes a new state-of-the-art performance in in-context imitation learning.

Robotics (cs.RO)

arXiv:2604.15215 [cs.RO]

(or arXiv:2604.15215v4 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.15215 Focus to learn more arXiv-issued DOI via DataCite

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