Gemini 2.5-Flash leads multimodal rapport estimation in real-world HRI study

A new arXiv paper explores multimodal rapport estimation in real-world Human-Robot Interaction (HRI) settings, moving beyond controlled lab environments. Researchers found that zero-shot Large Language Models (LLMs) performed well, with Gemini 2.5-Flash showing particular strength. A fusion model combining Gemini's text capabilities with audio (HuBERT) and visual (V-JEPA) models achieved the best overall performance, suggesting that a combination of modalities is crucial for accurate rapport estimation in dynamic, real-world scenarios. AI

IMPACT This research could lead to more adaptive and responsive robots in real-world interactions by improving their ability to gauge user engagement.

RANK_REASON The cluster contains an academic paper published on arXiv detailing new research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Akihiro Sakuramoto
• arXiv
• Gemini
• Gemini 2.5-Flash
• HuBERT
• Human–Robot Interaction
• V-JEPA

• paper
• other

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