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

Researchers have developed a lightweight transformer model that generates co-speech gestures for robots by predicting both semantic gesture placement and intensity from text and emotion signals alone, without requiring audio input at inference time. The model outperforms GPT-4o on the BEAT2 dataset for both gesture classification and intensity regression tasks. The approach is computationally efficient enough for real-time deployment on embodied agents, addressing a gap in current robot systems that typically produce only rhythmic beat-like motions rather than semantically meaningful gestures.