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arXiv · 2604.15215

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

Fawad Javed Fateh·Ali Shah Ali·Murad Popattia·Usman Nizamani·Andrey Konin·M. Zeeshan Zia·Quoc-Huy Tran
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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.

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Fawad Javed Fateh, Ali Shah Ali, Murad Popattia, Usman Nizamani, Andrey Konin, M. Zeeshan Zia, Quoc-Huy Tran. 2026-04-16. A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics. https://arxiv.org/abs/2604.15215

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