ActionPiece: Rethinking Action Tokenization for Autoregressive Vision-Language-Action Models
Action tokenizers play a central role in autoregressive vision-language-action (VLA) models, determining both the targets for policy training and the executable commands recovered from predicted tokens. Their fidelity is commonly evaluated using pointwise reconstruction metrics such as mean squared error (MSE), yet small individual errors do not fully characterize how faithfully action adjustments across demonstrations are preserved. After compression, similar actions may still cluster around a representative motion, while the adjustments needed for different contexts are diminished, distorted, or even reversed. We introduce physical rank consistency (PRC) to measure how well tokenization...
| Authors | Shijie Lian, Bin Yu, Zhaolong Shen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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