Large language models are increasingly being explored as interfaces between humans and robotic systems, yet there remains limited evidence on how such technologies can be used not only for interaction, but also as a structured means of introducing robotics to non-specialist users in real organizational settings. This paper introduces and evaluates a challenge-based method for robotics awareness, implemented through an LLM-enabled humanoid robot activity conducted with employees of AD Ports Group in the United Arab Emirates. In the event, participants engaged with a humanoid robot in a logistics-inspired task environment using voice commands interpreted through an LLM-based control framework. The activity was designed as a team-based, role-driven experience intended to expose participants to embodied AI and human-robot collaboration without requiring prior robotics expertise. To evaluate the approach, a post-event survey remained open for 16 days and collected 102 responses. Results indicate strong overall reception, with high satisfaction (8.46/10), increased interest in robotics and AI (4.47/5), and improved understanding of emerging forms of human-robot collaboration (4.45/5). Participants who interacted directly with the robot also reported natural interaction (4.37/5) and a strong sense that interaction became easier as the activity progressed (4.74/5). At the same time, lower ratings for reliability and predictability point to important technical and design challenges for future iterations. The findings suggest that challenge-based, LLM-enabled humanoid interaction can serve as a promising and replicable method for robotics awareness in industrial and operational environments.

翻译：大语言模型正越来越多地被探索作为人类与机器人系统之间的接口，然而，关于此类技术如何不仅用于交互，而且作为一种结构化的手段，在真实组织环境中向非专业用户引入机器人技术的证据仍然有限。本文介绍并评估了一种基于挑战赛的机器人认知方法，该方法通过在阿拉伯联合酋长国AD港口集团员工中开展的一项基于大语言模型（LLM）的人形机器人活动得以实施。在该活动中，参与者使用通过基于LLM的控制框架解析的语音命令，在后勤任务场景中与人形机器人互动。该活动设计为团队协作、角色驱动的体验，旨在让参与者在无需机器人技术先前知识的情况下，接触具身人工智能与人机协作。为评估该方法，一项活动后调查持续开放16天，共收集102份回复。结果显示整体反响强烈，满意度高（8.46/10），对机器人与人工智能的兴趣提升（4.47/5），以及对新兴人机协作形式理解加深（4.45/5）。直接与机器人互动的参与者还报告了自然的交互体验（4.37/5），并强烈感受到随着活动进行交互变得更容易（4.74/5）。同时，可靠性与可预测性方面的较低评分指出了未来迭代中重要的技术与设计挑战。研究结果表明，基于挑战赛的、由LLM驱动的人形机器人交互可作为一种在工业与运营环境中具有前景且可复制的机器人认知方法。