To facilitate human--robot interaction (HRI) tasks in real-world scenarios, service robots must adapt to dynamic environments and understand the required tasks while effectively communicating with humans. To accomplish HRI in practice, we propose a novel indoor dynamic map, task understanding system, and response generation system. The indoor dynamic map optimizes robot behavior by managing an occupancy grid map and dynamic information, such as furniture and humans, in separate layers. The task understanding system targets tasks that require multiple actions, such as serving ordered items. Task representations that predefine the flow of necessary actions are applied to achieve highly accurate understanding. The response generation system is executed in parallel with task understanding to facilitate smooth HRI by informing humans of the subsequent actions of the robot. In this study, we focused on waiter duties in a restaurant setting as a representative application of HRI in a dynamic environment. We developed an HRI system that could perform tasks such as serving food and cleaning up while communicating with customers. In experiments conducted in a simulated restaurant environment, the proposed HRI system successfully communicated with customers and served ordered food with 90\% accuracy. In a questionnaire administered after the experiment, the HRI system of the robot received 4.2 points out of 5. These outcomes indicated the effectiveness of the proposed method and HRI system in executing waiter tasks in real-world environments.

翻译：为促进真实场景中的人机交互任务，服务机器人必须适应动态环境、理解所需任务，并与人类进行有效沟通。为实现实际应用中的人机交互，本文提出了一种新颖的室内动态地图、任务理解系统与响应生成系统。室内动态地图通过将占据栅格地图与动态信息（如家具和人类）分层管理来优化机器人行为。任务理解系统针对需要多个动作的任务（如配送餐点），通过预定义必要动作流程的任务表示来实现高精度理解。响应生成系统与任务理解并行执行，通过向人类告知机器人后续动作来促进流畅的人机交互。本研究以餐厅场景中的服务员职责作为动态环境下人机交互的典型应用，开发了能够执行送餐、清理等任务并与顾客沟通的人机交互系统。在模拟餐厅环境进行的实验中，所提出的人机交互系统成功与顾客沟通并以90%的准确率完成餐点配送。实验后问卷调查显示，机器人的人机交互系统在5分制评分中获得4.2分。这些结果表明所提方法及人机交互系统在真实世界环境中执行服务员任务的有效性。