DiMaS: Distribution Matching for Steering Vision-Language-Action Models
Flow-matching-based vision-language-action (VLA) models have emerged as powerful policies for robotic manipulation, yet a critical capability remains underexplored: fine-grained behavioral control, the ability to govern how a robot performs a task by intervening on its internal representations. Representation steering is a well-established interpretability tool for language and vision-language models, where behavioral features are typically encoded as linear directions, but we show that these classic methods fall short in VLAs. We propose DiMaS, a Distribution-Matching Steering strategy tailored to flow-matching VLAs, which transports between representation distributions rather than shift...
| Authors | Pegah Khayatan, Sara Meziane, Jayneel Parekh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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