MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning

Visual navigation policies are widely regarded as a critical re-search direction, as they emulate human navigation behavior by leverag-ing egocentric visual observations. However, unlike LiDAR point cloudsor depth maps, visual observations do not provide explicit geometricinformation for navigation, especially in cluttered or dynamic environ-ments, which motivates the need for learning-based models and large-scale data. To this end, we propose to leverage Vision-Language-Action(VLA) models to learn diverse navigation capabilities from synthetic ex-pert data and to alleviate the sim-to-real gap by co-training on large-scalereal-world Visual Question Answering (VQA) data. Specifically, we de-velop MM-Nav, a 7B multi-view VLA model featuring custom-designedarchitectures and enabling a 7 Hz inference speed with 360◦ observation.For large-scale navigation data, we collect a total of 1.5 million expertdemonstrations from three reinforcement learning (RL) experts, eachtrained with privileged information in a challenging, tailor-made envi-ronment and specialized in one of three navigation capabilities: reaching,squeezing, and avoiding. We then iteratively train MM-Nav on these data,dynamically balancing the training data ratio across the three capabil-ities based on their respective performance. Through extensive exper-iments in synthetic and real-world environments, we demonstrate thatour model achieves strong performance and generalization on differentbenchmarks. MM-Nav obtains a success rate of 88.1% on the InternVLA-N1 System-1 point-goal navigation benchmark. Moreover, we find thatour student VLA model outperforms the RL teachers, demonstrating thesynergistic effect of integrating multiple capabilities. Extensive real-worldexperiments further confirm the effectiveness of our method.