HCSG combines geometric forecasting of human pose and trajectory with VLM-generated semantic descriptions of intentions, fused into a topological map with a social distance loss, yielding 14% higher success rate and 34% lower collision rate on the HA-VLNCE benchmark.
P3nav: End-to-end perception, prediction and planning for vision-and-language navigation
3 Pith papers cite this work. Polarity classification is still indexing.
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SEDualVLN introduces a spatially-enhanced dual-system VLN architecture that achieves state-of-the-art results on VLN-CE benchmarks through coordinated VLM action generation and MLLM waypoint planning.
StereoNav reaches new benchmark highs on R2R-CE and RxR-CE and improves real-robot reliability by supplying persistent target-location priors and stereo-derived geometry that stay stable under lighting changes and blur.
citing papers explorer
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HCSG: Human-Centric Semantic-Geometric Reasoning for Vision-Language Navigation
HCSG combines geometric forecasting of human pose and trajectory with VLM-generated semantic descriptions of intentions, fused into a topological map with a social distance loss, yielding 14% higher success rate and 34% lower collision rate on the HA-VLNCE benchmark.
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SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation
SEDualVLN introduces a spatially-enhanced dual-system VLN architecture that achieves state-of-the-art results on VLN-CE benchmarks through coordinated VLM action generation and MLLM waypoint planning.
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What Limits Vision-and-Language Navigation ?
StereoNav reaches new benchmark highs on R2R-CE and RxR-CE and improves real-robot reliability by supplying persistent target-location priors and stereo-derived geometry that stay stable under lighting changes and blur.