ImagineNav++ achieves SOTA mapless visual navigation by prompting VLMs to select imagined future views generated from a human-preference-distilled module and maintained via selective foveation memory.
Multimodal large lan- guage model for visual navigation
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NaVid, a video-based VLM trained on 510k navigation and 763k web samples, achieves SOTA VLN performance using only monocular RGB video for next-step action planning in sim and real environments.
IntentNav is a spatial-visual imitation framework that infers human search intent via frontier labeling to train VLM policies for object navigation, reporting SOTA on MP3D and HM3D benchmarks with zero-shot transfer to wheeled, quadruped, and humanoid robots.
citing papers explorer
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ImagineNav++: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination
ImagineNav++ achieves SOTA mapless visual navigation by prompting VLMs to select imagined future views generated from a human-preference-distilled module and maintained via selective foveation memory.
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NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
NaVid, a video-based VLM trained on 510k navigation and 763k web samples, achieves SOTA VLN performance using only monocular RGB video for next-step action planning in sim and real environments.
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IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
IntentNav is a spatial-visual imitation framework that infers human search intent via frontier labeling to train VLM policies for object navigation, reporting SOTA on MP3D and HM3D benchmarks with zero-shot transfer to wheeled, quadruped, and humanoid robots.