arXiv:2610.35615 · v1

Context-Aware Generation and Modulation of Expressive Motion Behavior using Multimodal Foundation Models

首次提交 2026-10-26 23:33

版本更新 2026-10-26 23:33

雷达观测 2026-10-29 00:35

摘要

Expressive robot motion positively impacts human-robot interaction by improving user engagement, likability, or task performance. We present a novel approach to automatically generate and modulate complex, expressive full-body gesture sequences from a multimodal (text, audio, video) context description. Based on a unified mathematical implementation of the Principles of Animation using Dynamic Movement Primitives, we use multimodal foundation models to generate such sequences along with parametric motion variations that are highly context-aware. This method extends the state of the art in terms of flexibility and generality by being interpretable, composable, and working across different robot morphologies. Moreover, we integrate the system into a continuous control framework and leverage knowledge distillation to learn a much smaller model, significantly improving token efficiency and system latency. Results from a user study with a human-like platform indicate that participants judged our system's motions to be better aligned with the interaction context than motions produced under a non-modulated or API-level condition. We also demonstrate how the system generates expressive motion for robots with different kinematics, showcasing its versatility. Paper webpage: https://gen-mod-expressive-motion-behavior.github.io/.

期刊信息：Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, 2026, pp. 438-446