OptiSight: Bridging Semantic Reasoning and Geometric Control for Embodied Navigation
Autonomous indoor navigation requires both semantic understanding and precise geometric control. We propose OptiSight, a hybrid framework that combines Vision-Language Model reasoning with deterministic visual servoing through a finite-state Chain-of-Thought architecture. Grounded-SAM localizes open-vocabulary targets, while camera projection geometry converts visual observations into navigation commands without requiring dense mapping. The VLM is queried only at key decision points, reducing computational overhead while geometric control handles continuous navigation. Experiments in AI Habita
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- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “OptiSight: Bridging Semantic Reasoning and Geometric Control” ≈ “lllyasviel/ControlNet-v1-1””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “OptiSight: Bridging Semantic Reasoning and Geometric Control” ≈ “lllyasviel/ControlNet””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “OptiSight: Bridging Semantic Reasoning and Geometric Control” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Alperen Avan →
“OptiSight: Bridging Semantic Reasoning and Geometric Control for Embodied Navigation”
- LinkedLinked via arxiv author · 85%Jordi Sanchez-Riera →
“OptiSight: Bridging Semantic Reasoning and Geometric Control for Embodied Navigation”