Abstract: Embodied Navigation is a fundamental capability for intelligent robots, requiring robots to follow human commands and move autonomously within physical environments. Despite significant advancements, most existing navigation approaches are tailored to specific navigation tasks, such as instruction following, searching objects, answering questions, tracking people, and more. However, the increasing demands on advanced embodied navigation pose the challenge of designing a practical navigation agent that can incorporate multiple navigation tasks naturally and benefits from the synergy between these tasks. To this end, we present Uni-NaVid, a video-based vision-language-action (VLA) model to unify different paradigms of navigation tasks and improve navigation performance by encouraging the synergy among different navigation sub-tasks. This VLA model can directly take natural language instructions and RGB video streams as inputs and output low-level robotic actions in an end-to-end manner. To efficiently process extensive RGB video streams, we propose an online token merge strategy that spatially and temporally consolidates similar visual information which improves the inference speed to 5 Hz. For training Uni-NaVid, we collect 3.6 million navigation data samples across different navigation tasks. Extensive experiments on diverse navigation benchmarks demonstrate that Uni-NaVid achieves state-of-the-art performance within a unified framework by using only ego-centric RGB video as inputs. Additionally, real-world experiments confirm the model’s effectiveness and efficiency, shedding light on its strong generalizability.