Nadine：一个由LLM驱动的、具备情感能力与类人记忆的智能社交机器人
Nadine: An LLM-driven Intelligent Social Robot with Affective Capabilities and Human-like Memory
AI总结：
本研究为Nadine社交机器人平台提出由LLM驱动的SoR-ReAct智能体框架，通过集成多模态输入处理、类人长期记忆与情感评估模块，实现了具备类人情感与认知能力的社交机器人系统，提升了人机交互质量。
摘要：
在本研究中，我们描述了为Nadine社交机器人平台开发智能且稳健的社交机器人系统的方法。我们通过集成大型语言模型（LLMs）并巧妙利用此类模型强大的推理和指令遵循能力，来实现高级的类人情感与认知能力。与当前最先进的基于LLM的智能体相比，这种方法具有新颖性，因为后者并未实现类人的长期记忆或复杂的情感评估。由多个模块组成的社交机器人的自然度，高度依赖于系统每个组件的性能与能力，以及这些组件的无缝集成。我们构建的社交机器人系统能够通过多模态输入处理生成适当的行为，根据识别出的用户调取相应的情景记忆，并模拟由与人类伙伴交互所引发的机器人情绪状态。特别地，我们为社交机器人引入了一个LLM智能体框架SoR-ReAct，作为我们系统中交互模块的核心组件。这一设计推动了社交机器人的发展，旨在提升人机交互的质量。
英文摘要：
In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating Large Language Models (LLMs) and skilfully leveraging the powerful reasoning and instruction-following capabilities of these types of models to achieve advanced human-like affective and cognitive capabilities. This approach is novel compared to the current state-of-the-art LLM-based agents which do not implement human-like long-term memory or sophisticated emotional appraisal. The naturalness of social robots, consisting of multiple modules, highly depends on the performance and capabilities of each component of the system and the seamless integration of the components. We built a social robot system that enables generating appropriate behaviours through multimodal input processing, bringing episodic memories accordingly to the recognised user, and simulating the emotional states of the robot induced by the interaction with the human partner. In particular, we introduce an LLM-agent frame for social robots, SoR-ReAct, serving as a core component for the interaction module in our system. This design has brought forth the advancement of social robots and aims to increase the quality of human-robot interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
