NaviTrace: Evaluating Embodied Navigation of Vision-Language Models
Authors: name: Tim Windecker; name: Manthan Patel; name: Moritz Reuss; name: Richard Schwarzkopf; name: Cesar Cadena; name: Rudolf Lioutikov; name: Marco Hutter; name: Jonas Frey
Published: 2025-10-30 18:16:32

Abstract: Vision-language models demonstrate unprecedented performance and
generalization across a wide range of tasks and scenarios. Integrating these
foundation models into robotic navigation systems opens pathways toward
building general-purpose robots. Yet, evaluating these models' navigation
capabilities remains constrained by costly real-world trials, overly simplified
simulations, and limited benchmarks. We introduce NaviTrace, a high-quality
Visual Question Answering benchmark where a model receives an instruction and
embodiment type (human, legged robot, wheeled robot, bicycle) and must output a
2D navigation trace in image space. Across 1000 scenarios and more than 3000
expert traces, we systematically evaluate eight state-of-the-art VLMs using a
newly introduced semantic-aware trace score. This metric combines Dynamic Time
Warping distance, goal endpoint error, and embodiment-conditioned penalties
derived from per-pixel semantics and correlates with human preferences. Our
evaluation reveals consistent gap to human performance caused by poor spatial
grounding and goal localization. NaviTrace establishes a scalable and
reproducible benchmark for real-world robotic navigation. The benchmark and
leaderboard can be found at
https://leggedrobotics.github.io/navitrace_webpage/.