This paper describes a system for service robots that combines ontological knowledge reasoning and human–robot interaction to interpret natural language commands and successfully perform household chores, such as finding and delivering objects. Knowledge and context reasoning is essential for providing more efficient service robots, given their diverse and continuously changing environments. Moreover, since they are in contact with humans, robots require such skills as interaction and language. Therefore, we developed a system with specific modules to manage robots’ knowledge and reasoning, command analysis, decision-making, and talking interaction. The system relies on inference methods and verbal interaction to understand commands and clarify uncertain information. We tested our system inside a simulated environment where the robot receives commands with missing or unclear information. The system’s performance was compared with the average performance of human subjects who completed the same commands in the simulation.

本文介绍了一种用于服务机器人的系统，该系统结合了本体论知识推理和人机交互功能，以解释自然语言命令并成功执行家务劳动，例如查找和交付对象。鉴于环境不断变化，不断变化的知识和情境推理对于提供更高效的服务机器人至关重要。此外，由于机器人与人接触，因此机器人需要诸如交互和语言之类的技能。因此，我们开发了一个具有特定模块的系统，用于管理机器人的知识和推理，命令分析，决策和对话交互。该系统依靠推理方法和言语交互来理解命令并澄清不确定的信息。我们在模拟环境中测试了我们的系统，在该环境中机器人会接收到缺少或不清楚信息的命令。将系统的性能与在模拟中完成相同命令的人类受试者的平均性能进行了比较。