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.
Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation,
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LiveVLN enables smoother vision-language navigation by overlapping action execution with ongoing observation processing, preserving benchmark scores while cutting real-world waiting time by up to 77.7 percent.
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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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LiveVLN: Breaking the Stop-and-Go Loop in Vision-Language Navigation
LiveVLN enables smoother vision-language navigation by overlapping action execution with ongoing observation processing, preserving benchmark scores while cutting real-world waiting time by up to 77.7 percent.