Sandris Dubovs V L Nav Neka 【Secure】

"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

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)

"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) Sandris Dubovs V L Nav Neka

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

You can find the full technical details on arXiv: VL-Nav . "In rigorous testing, including the , VL-Nav achieved

Uses a CVL (Curiosity-driven Vision-Language) score to prioritize exploring unknown areas that align with human descriptions.

View demonstrations on robots like the Unitree G1 and Go2 at the SAIR Lab Project Page . You can find the full technical details on arXiv: VL-Nav

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