SpaceVLN proposes a stagewise closed-loop framework using Spatial Cognitive Memory and Spatial-CoT for zero-shot vision-and-language navigation and object-goal navigation, reporting SOTA results on R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON plus real-robot tests.
One agent to guide them all: Empowering mllms for vision-and-language navigation via explicit world representation.arXiv preprint arXiv:2602.15400, 2026
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Proposes cost-aware question selection for ambiguous object navigation via information-gain analysis on corpora, a cost-penalizing benchmark, and a zero-shot MLLM agent.
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SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
SpaceVLN proposes a stagewise closed-loop framework using Spatial Cognitive Memory and Spatial-CoT for zero-shot vision-and-language navigation and object-goal navigation, reporting SOTA results on R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON plus real-robot tests.
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Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation
Proposes cost-aware question selection for ambiguous object navigation via information-gain analysis on corpora, a cost-penalizing benchmark, and a zero-shot MLLM agent.