服务机器人利用开放知识理解用户指令
Understanding User Instructions by Utilizing Open Knowledge for Service Robots
摘要：
理解自然语言中的用户指令是人工智能和机器人领域的一个活跃的研究课题。通常，自然用户指令是高级的，并且可以被简化为以常见动词（例如，“take”、“get”、“put”）。对于机器人理解这些指令，一个关键的挑战是处理高层用户指令，并实现指定的任务与机器人的原始行动。为了解决这个问题，我们提出了新的算法，利用语义词典中定义的常见动词的语义角色，并集成多个开放的知识来生成任务计划。具体来说，我们提出了一种新的方法，用于匹配和恢复语义的用户指令和一个新的任务规划器，利用机器人的动作模型的功能知识。为了验证和评估我们的方法，我们实现了一个原型系统使用的知识，从几个开放的资源。在我们的系统上的实验证实了我们的算法的正确性和效率。值得注意的是，我们的系统已经部署在科佳机器人，参加了年度RoboCup@Home比赛在过去三年中，并取得了令人瞩目的高分数的基准测试。
英文摘要：
Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., `take', `get', `put'). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with robots' primitive actions. To address this, we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans. Specifically, we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot's action model. To verify and evaluate our approach, we implemented a prototype system using knowledge from several open resources. Experiments on our system confirmed the correctness and efficiency of our algorithms. Notably, our system has been deployed in the KeJia robot, which participated the annual RoboCup@Home competitions in the past three years and achieved encouragingly high scores in the benchmark tests.
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。
