GeniNav: Generative Model Driven Image-Goal Navigation via Imagination-Guided Consistency Flow Matching
Chen, Yuqi, Gao, Junjie, Pan, Yongzhou, Song, Siyuan, Zhang, Zixuan, Xiao, Jiaping, Feroskhan, Mir
原文摘要
Image-goal navigation driven by generative models has recently shown strong potential owing to their ability to perform multi-modal reasoning and stable learning in continuous control spaces. Despite their promise, current methods still face several fundamental limitations. Many rely on pre-built priors and lack explicit mechanisms for trajectory evaluation, restricting generalization and goal alignment in map-free navigation. Moreover, current generative policies often face inefficiency or temporal inconsistency, resulting in temporally unstable motion. The absence of interactive, closed-loop benchmarks further limits fair and reproducible comparison. To address these issues, we propose GeniNav, a generative image-goal navigation framework that couples a VLM-driven latent subgoal imagination module for high-level semantic guidance with Multi-Segment Consistency Flow Matching (MS-CFM) for temporally smooth and dynamically coherent motion generation. A hybrid trajectory evaluation module further integrates semantic alignment and geometric feasibility to assess goal consistency. We also introduce a closed-loop simulation benchmark with a large-scale dataset spanning 176 scenes and 491.6 km for standardized training and evaluation. Extensive experiments in simulation and on real robots demonstrate the effectiveness of our method. Our project page is available at: https://cyq638.github.io/geninav/.
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表