Direction-Scale Decomposition in Action Representation: Rethinking What to Tokenize for Vision-Language-Action Models
Action representation plays a central role in discrete-token vision-language-action (VLA) learning but remains underexamined. Under conventional pose-increment representations, action tokens are sensitive to execution speed and dataset-specific normalization, potentially obscuring geometric structure shared across demonstrations and datasets. We introduce Direction-Scale Decomposition (DSD), an action representation that decomposes translation and rotation increments into direction and scale components before tokenization. DSD isolates motion direction while retaining magnitudes in separate scale channels. We evaluate DSD with uniform binning (BIN) and BEAST, a B-spline-based tokenizer, i...
| Authors | Yufei Duan, Hang Yin, Alberta Longhini et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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