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This research introduces WaveSync, a hybrid framework leveraging Large Language Models and Dynamic Movement Primitives to generate kinematically feasible co-speech gestures on physical humanoid robots. By decomposing dialogue into semantic waves and optimizing gesture timing against speech emphasis, the system effectively bridges the gap between virtual avatar expressiveness and real-world actuator constraints. The study demonstrates that integrating these AI-driven planning stages significantly improves gesture-speech alignment in complex interaction scenarios.
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WaveSync: Constrained Wavefront Optimization for Synchronized Co-Speech Gestures in Humanoid Robots

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2606.16600v2 Announce Type: replace Abstract: Expressive co-speech gestures are crucial for natural human--robot interaction, yet generating them on physical humanoid robots remains challenging because, unlike virtual avatars, robots must synchronize gestures with speech under strict kinematic and actuator constraints. We present \textbf{WaveSync}, a hybrid framework in which a Large Language Model decomposes dialogue responses into structured semantic schemas and assigns per-word importance weights, forming a continuous Semantic Importance Wave. Gesture trajectories are shaped through Dynamic Movement Primitives to ensure kinematic feasibility while enhancing expressiveness. A Wavefront Optimization stage aligns gesture stroke peaks with speech emphasis peaks and resolves residual temporal conflicts through gesture-duration compression and forward propagation. Experimental evaluation across five dialogue scenarios demonstrates effective gesture--speech alignment and favorable performance in both objective and subjective evaluations. The results further show that the key components of WaveSync contribute to producing gestures that are expressive, semantically grounded, and kinematically feasible. The code, resources, and videos are available at \href{https://github.com/pairs-lab/WaveSync}{WaveSync}.

Abstract:Expressive co-speech gestures are crucial for natural human--robot interaction, yet generating them on physical humanoid robots remains challenging because, unlike virtual avatars, robots must synchronize gestures with speech under strict kinematic and actuator constraints. We present \textbf{WaveSync}, a hybrid framework in which a Large Language Model decomposes dialogue responses into structured semantic schemas and assigns per-word importance weights, forming a continuous Semantic Importance Wave. Gesture trajectories are shaped through Dynamic Movement Primitives to ensure kinematic feasibility while enhancing expressiveness. A Wavefront Optimization stage aligns gesture stroke peaks with speech emphasis peaks and resolves residual temporal conflicts through gesture-duration compression and forward propagation. Experimental evaluation across five dialogue scenarios demonstrates effective gesture--speech alignment and favorable performance in both objective and subjective evaluations. The results further show that the key components of WaveSync contribute to producing gestures that are expressive, semantically grounded, and kinematically feasible. The code, resources, and videos are available at \href{this https URL}{WaveSync}.

Robotics (cs.RO)

arXiv:2606.16600 [cs.RO]

(or arXiv:2606.16600v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.16600 Focus to learn more arXiv-issued DOI via DataCite

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