Ordered Action Tokens for Visuomotor Policy Learning
Action tokenization maps continuous robot action chunks to discrete tokens and has become an important interface for modern visuomotor policies. Existing approaches either rely on analytical discretization methods that produce prohibitively long token sequences or learned latent tokenizers that lack structure, limiting their compatibility with downstream policies. In this work, we identify three desiderata for action tokenization - high compression, total decodability, and an ordered token space - and introduce Ordered Action Tokenization (OAT), a learned action tokenizer that satisfies all three. OAT discretizes action chunks into an ordered sequence of tokens using a transformer with re...
| Authors | Chaoqi Liu, Yue Zhao, Haonan Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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