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.
Vision and language navigation in the real world via online visual language mapping
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MAGNIFIED applies RL fine-tuning to MLLMs for autonomous driving motion planning, yielding over 10.5% lower overlap rate and 38.9% lower off-road rate than SFT baseline on Waymo Open Motion Dataset.
HSAN integrates hierarchical semantic graphs, optimal transport-based goal selection, and graph-aware RL to claim SOTA results on VLN-CE tasks.
citing papers explorer
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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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MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
MAGNIFIED applies RL fine-tuning to MLLMs for autonomous driving motion planning, yielding over 10.5% lower overlap rate and 38.9% lower off-road rate than SFT baseline on Waymo Open Motion Dataset.
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Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation
HSAN integrates hierarchical semantic graphs, optimal transport-based goal selection, and graph-aware RL to claim SOTA results on VLN-CE tasks.