Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation
Robots collaborating with humans must convert natural language goals into actionable, physically grounded decisions. For example, executing a command such as "go two meters to the right of the fridge" requires grounding semantic references, spatial relations, and metric constraints within a 3D scene. While recent vision language models (VLMs) demonstrate strong semantic grounding capabilities, they are not explicitly designed to reason about metric constraints in physically defined spaces. In this work, we empirically demonstrate that state-of-the-art VLM-based grounding approaches struggle with complex metric-semantic language queries. To address this limitation, we propose MAPG (Multi-A...
| Authors | Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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