SALSA aligns social features and adds future-risk signals in VLA models to cut near-collisions by 86.4% and raise social accuracy from 53% to 93% on SCAND and real robots.
VLM -Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models
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The Semantic Autonomy Stack combines a seven-step parametric resolver handling 88% of instructions in under 0.1 ms with VLM escalation and a five-category cross-robot memory system, achieving 100% accuracy and 103,000-fold latency reduction on Raspberry Pi 5 robots with no GPU or training data.
citing papers explorer
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Act on What You See: Unlocking Safe Social Navigation in Vision-Language-Action Models
SALSA aligns social features and adds future-risk signals in VLA models to cut near-collisions by 86.4% and raise social accuracy from 53% to 93% on SCAND and real robots.
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A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory
The Semantic Autonomy Stack combines a seven-step parametric resolver handling 88% of instructions in under 0.1 ms with VLM escalation and a five-category cross-robot memory system, achieving 100% accuracy and 103,000-fold latency reduction on Raspberry Pi 5 robots with no GPU or training data.