MistyPilot: 一种面向Misty社交机器人的代理快速-缓慢思考LLM框架
MistyPilot: An Agentic Fast-Slow Thinking LLM Framework for Misty Social Robots
AI总结：
MistyPilot通过代理驱动的LLM框架，实现社交机器人中工具选择、协调及参数配置的高效执行，提升任务效率与情感一致性。
摘要：
随着社交机器人开放API的出现，定制通用工具以满足用户需求变得更加容易。然而，对于没有编程经验的用户来说，解释高层用户指令、选择和配置合适的工具以及可靠地执行这些任务仍然具有挑战性。为了解决这些挑战，我们介绍了MistyPilot，一种用于自主工具选择、协调和参数配置的代理驱动LLM框架。MistyPilot包含两个核心组件：物理交互代理（PIA）和社会智能代理（SIA）。PIA能够实现稳健的传感器触发和工具驱动的任务执行，而SIA则生成具有社会智能和情感一致性的对话。MistyPilot进一步集成了快速-缓慢思考范式，以捕捉用户偏好、减少延迟并提高任务效率。为了全面评估MistyPilot，我们贡献了五个基准数据集。广泛的实验表明，我们的框架在路由正确性、任务完整性、快速-缓慢思考检索效率、工具可扩展性和情感一致性方面均有效。所有代码、数据集和实验视频都将公开在项目网页上。
英文摘要：
With the availability of open APIs in social robots, it has become easier to customize general-purpose tools to meet users' needs. However, interpreting high-level user instructions, selecting and configuring appropriate tools, and executing them reliably remain challenging for users without programming experience. To address these challenges, we introduce MistyPilot, an agentic LLM-driven framework for autonomous tool selection, orchestration, and parameter configuration. MistyPilot comprises two core components: a Physically Interactive Agent (PIA) and a Socially Intelligent Agent (SIA). The PIA enables robust sensor-triggered and tool-driven task execution, while the SIA generates socially intelligent and emotionally aligned dialogue. MistyPilot further integrates a fast-slow thinking paradigm to capture user preferences, reduce latency, and improve task efficiency. To comprehensively evaluate MistyPilot, we contribute five benchmark datasets. Extensive experiments demonstrate the effectiveness of our framework in routing correctness, task completeness, fast-slow thinking retrieval efficiency, tool scalability,and emotion alignment. All code, datasets, and experimental videos will be made publicly available on the project webpage.
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。
