RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation
Safe and robust robot navigation across diverse environments requires a high-level understanding of complex scenes and the ability to carry it into stable motion. Recent works tackle this with learning-based models trained at scale and with approaches built on vision-language models (VLMs). However, learning-based models break down outside their training distribution, while VLM-based approaches bring that understanding but rarely ground it in the scene or align the action with it. To address these limitations, we present RECAST, a robot navigation framework that combines the reasoning of a VLM with the spatial grounding of vision foundation models to build an Actionable Cost map. Given th...
| Authors | Incheol Cho, Jintae Park, Jinkyu Kim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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