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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DM³-Nav delivers decentralized multi-agent semantic navigation for multimodal open-vocabulary multi-object tasks that matches centralized baselines in simulation and succeeds in real-world robot deployments.
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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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DM$^3$-Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation
DM³-Nav delivers decentralized multi-agent semantic navigation for multimodal open-vocabulary multi-object tasks that matches centralized baselines in simulation and succeeds in real-world robot deployments.