In this paper, we introduce a robotic agent specifically designed to analyze external environments and address participants' questions. The primary focus of this agent is to assist individuals using language-based interactions within video-based scenes. Our proposed method integrates video recognition technology and natural language processing models within the robotic agent. We investigate the crucial factors affecting human-robot interactions by examining pertinent issues arising between participants and robot agents. Methodologically, our experimental findings reveal a positive relationship between trust and interaction efficiency. Furthermore, our model demonstrates a 2\% to 3\% performance enhancement in comparison to other benchmark methods.

翻译：本文提出了一种专门用于分析外部环境并回答参与者问题的机器人代理。该代理的主要功能是在基于视频的场景中通过语言交互为个体提供帮助。我们提出的方法在机器人代理中集成了视频识别技术与自然语言处理模型。通过研究参与者与机器人代理之间出现的相关问题，我们探讨了影响人机交互的关键因素。在方法论上，实验结果表明，信任与交互效率之间存在正相关关系。此外，与其他基准方法相比，我们的模型性能提升了2%到3%。