用大模型提升机器人情感与视觉表达，让互动更真实有共情。
EVOLVE: Emotion and Visual Output Learning via LLM Evaluation
- 用大模型评估并选择情感化动作与视觉表现
- 结合视觉语言模型实现更开放的情感响应
- 适合社交机器人、人机交互研究者参考
人类对社交机器人的接受度很大程度上取决于其共情能力与被理解的感觉。这要求系统能对用户输入做出准确且灵活的反应。随着系统状态或回应类型增多，复杂性上升，而大语言模型在人机交互中的应用为感知与应答流程带来简化。通过大模型选择动作和情感表达，可增强展示出的共情真实性，改善机器人与用户间的沟通。不仅限于言语回应，本工作进一步探索大模型在具身化、现实场景中驱动非语言行为的可能性。我们拓展了基于大模型的社交机器人非语言行为研究，采用视觉-语言模型支持更开放的情感响应选择，并同步优化动作与色彩模式以强化意义传达与共情效果。
原文摘要 · Abstract (English)
Human acceptance of social robots is greatly effected by empathy and perceived understanding. This necessitates accurate and flexible responses to various input data from the user. While systems such as this can become increasingly complex as more states or response types are included, new research in the application of large language models towards human-robot interaction has allowed for more streamlined perception and reaction pipelines. LLM-selected actions and emotional expressions can help reinforce the realism of displayed empathy and allow for improved communication between the robot and user. Beyond portraying empathy in spoken or written responses, this shows the possibilities of using LLMs in actuated, real world scenarios. In this work we extend research in LLM-driven nonverbal behavior for social robots by considering more open-ended emotional response selection leveraging new advances in vision-language models, along with emotionally aligned motion and color pattern selections that strengthen conveyance of meaning and empathy.
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