VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Models
Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, however, their behavior often depends sharply on how the instruction is phrased, suggesting that language is not merely a task label but an optimizable conditioning input. We study whether frozen VLA policies can be improved by optimizing language space rather than updating action weights. Our method introduces a language-conditioning space policy that translates a human instruction into a short VLA-grounded command using object appearance, spatial relations, and target-grounding cues. The language-conditioning space policy is optimized wit...
| Authors | Damir Shodiev, Nikita Kachaev, Alexey K. Kovalev et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 7 October 2026 |
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