ERR@HRI 2.0 挑战赛：人机对话中错误与故障的多模态检测
ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
AI总结：
该挑战赛提供多模态数据集，旨在通过机器学习模型检测LLM驱动对话机器人在人机交互中的错误，以减少对话中断并提升用户信任。
摘要：
将大型语言模型（LLM）集成到对话机器人中使得人机对话更加动态化。然而，基于LLM的对话机器人仍然容易出错，例如误解用户意图、过早打断用户或完全无法响应。检测并解决这些故障对于防止对话中断、避免任务干扰以及维持用户信任至关重要。为解决这一问题，ERR@HRI 2.0 挑战赛提供了一个包含LLM驱动的对话机器人在人机对话中故障的多模态数据集，并鼓励研究人员对旨在检测机器人故障的机器学习模型进行基准测试。该数据集包含16小时的二元人机交互，涵盖了面部、语音和头部运动特征。每次交互都从系统角度标注了机器人错误的存在与否，以及感知到的用户意图，以纠正机器人行为与用户期望之间的不匹配。参赛者被邀请组建团队，开发利用多模态数据检测这些故障的机器学习模型。提交的模型将使用多种性能指标进行评估，包括检测准确率和假阳性率。该挑战赛代表了通过社会信号分析改进人机交互中故障检测的又一关键步骤。
英文摘要：
The integration of large language models (LLMs) into conversational robots has made human-robot conversations more dynamic. Yet, LLM-powered conversational robots remain prone to errors, e.g., misunderstanding user intent, prematurely interrupting users, or failing to respond altogether. Detecting and addressing these failures is critical for preventing conversational breakdowns, avoiding task disruptions, and sustaining user trust. To tackle this problem, the ERR@HRI 2.0 Challenge provides a multimodal dataset of LLM-powered conversational robot failures during human-robot conversations and encourages researchers to benchmark machine learning models designed to detect robot failures. The dataset includes 16 hours of dyadic human-robot interactions, incorporating facial, speech, and head movement features. Each interaction is annotated with the presence or absence of robot errors from the system perspective, and perceived user intention to correct for a mismatch between robot behavior and user expectation. Participants are invited to form teams and develop machine learning models that detect these failures using multimodal data. Submissions will be evaluated using various performance metrics, including detection accuracy and false positive rate. This challenge represents another key step toward improving failure detection in human-robot interaction through social signal analysis.
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。
