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

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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

  1. arXiv cs.CL TIER_1 English(EN) · Akihiro Sakuramoto, Takato Hayashi, Ryo Miyoshi, Yuki Okafuji, Shogo Okada ·

    Multimodal Rapport Estimation in Real-World HRI

    arXiv:2608.18401v1 Announce Type: cross Abstract: Evaluating interaction quality in real-world HRI is an important challenge. If interaction quality can be estimated reliably, the results can be used to improve dialogue strategies and ultimately enable robots to adapt their behav…

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