Sandris Dubovs V L Nav NekaSandris Dubovs V L Nav NekaSandris Dubovs V L Nav NekaSandris Dubovs V L Nav NekaSandris Dubovs V L Nav NekaSandris Dubovs V L Nav Neka

Guías y tutoriales

En esta sección le presentamos guías y tutoriales para realizar las configuraciones más comunes y resolver los problemas más frecuentes que presentan nuestros usuarios de acuerdo a la experiencia de nuestra área de soporte. Para visualizar una guía haga clic en el título. Agregamos guías constantemente de acuerdo a la necesidad. Revise ésta página con frecuencia.

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Sandris Dubovs V L Nav Neka May 2026

is an advanced robotic navigation framework that combines neural reasoning (the "brain") with symbolic guidance (the "logic") to help robots navigate complex environments. Unlike traditional methods that might lead to aimless wandering, VL-Nav uses a NeSy (Neuro-Symbolic) Task Planner and an Exploration System to understand abstract human instructions. Useful Text Blocks 1. The "Problem & Solution" Pitch (Good for Intros)

You can find the full technical details on arXiv: VL-Nav . Sandris Dubovs V L Nav Neka

Uses a CVL (Curiosity-driven Vision-Language) score to prioritize exploring unknown areas that align with human descriptions. is an advanced robotic navigation framework that combines

"Traditional robot navigation often fails when faced with complex, multi-step instructions or unknown environments, resulting in inefficient 'aimless wandering.' addresses this by intertwining neural semantic understanding with symbolic 3D scene graphs. This allows the robot to decompose abstract commands—like finding a waterproof jacket based on a rain report—into logical navigation goals." 2. Key Technical Features (Good for Specs) The "Problem & Solution" Pitch (Good for Intros)

View demonstrations on robots like the Unitree G1 and Go2 at the SAIR Lab Project Page .

"In rigorous testing, including the , VL-Nav achieved a 75–83% success rate across indoor and outdoor settings. In real-world deployments, it maintained an 86.3% success rate , demonstrating reliability over long-range trajectories of up to 483 meters." Resources for Further Development