Sandris Dubovs V L Nav Neka Here

Proven to navigate successfully across different floors and transitions (e.g., using elevators or stairs) in complex building layouts. 3. Performance Summary (Good for Validation)

For related open-source frameworks, check repositories like oobvlm on GitHub.

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) Sandris Dubovs V L Nav Neka

"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)

View demonstrations on robots like the Unitree G1 and Go2 at the SAIR Lab Project Page . Proven to navigate successfully across different floors and

You can find the full technical details on arXiv: VL-Nav .

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

Leverages a 3D scene graph and image memory to help Vision Language Models (VLMs) replan tasks in real-time.