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arXiv · 2604.11417

Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech

Edwin C. Montiel-Vazquez·Christian Arzate Cruz·Stefanos Gkikas·Thomas Kassiotis·Giorgos Giannakakis·Randy Gomez
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Abstract

Co-speech gestures increase engagement and improve speech understanding. Most data-driven robot systems generate rhythmic beat-like motion, yet few integrate semantic emphasis. To address this, we propose a lightweight transformer that derives iconic gesture placement and intensity from text and emotion alone, requiring no audio input at inference time. The model outperforms GPT-4o in both semantic gesture placement classification and intensity regression on the BEAT2 dataset, while remaining computationally compact and suitable for real-time deployment on embodied agents.

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Edwin C. Montiel-Vazquez, Christian Arzate Cruz, Stefanos Gkikas, Thomas Kassiotis, Giorgos Giannakakis, Randy Gomez. 2026-04-13. Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech. https://arxiv.org/abs/2604.11417

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