Synthesizing Robot Programs with Interactive Tutor Mode  

作　　者：Hao Li Yu-Ping Wang Tai-Jiang Mu 

机构地区：[1]Tsinghua National Laboratory for Information Science and Technology(TNList),Department of Computer Science and Technology,Tsinghua University

出　　处：《International Journal of Automation and computing》2019年第4期462-474,共13页国际自动化与计算杂志（英文版）

基　　金：supported by Tsinghua University Initiative Scientific Research Program(No.20141081140)

摘　　要：With the rapid development of the robotic industry, domestic robots have become increasingly popular. As domestic robots are expected to be personal assistants, it is important to develop a natural language-based human-robot interactive system for end-users who do not necessarily have much programming knowledge. To build such a system, we developed an interactive tutoring framework, named " Holert”, which can translate task descriptions in natural language to machine-interpretable logical forms automatically. Compared to previous works, Holert allows users to teach the robot by further explaining their intentions in an interactive tutor mode. Furthermore, Holert introduces a semantic dependency model to enable the robot to " understand” similar task descriptions. We have deployed Holert on an open-source robot platform, Turtlebot 2. Experimental results show that the system accuracy could be significantly improved by 163.9% with the support of the tutor mode. This system is also efficient. Even the longest task session with 10 sentences can be handled within 0.7 s.With the rapid development of the robotic industry,domestic robots have become increasingly popular.As domestic robots are expected to be personal assistants,it is important to develop a natural language-based human-robot interactive system for end-users who do not necessarily have much programming knowledge.To build such a system,we developed an interactive tutoring framework,named "Holert",which can translate task descriptions in natural language to machine-interpretable logical forms automatically.Compared to previous works,Holert allows users to teach the robot by further explaining their intentions in an interactive tutor mode.Furthermore,Holert introduces a semantic dependency model to enable the robot to "understand" similar task descriptions.We have deployed Holert on an open-source robot platform,Turtlebot 2.Experimental results show that the system accuracy could be significantly improved by 163.9% with the support of the tutor mode.This system is also efficient.Even the longest task session with 10 sentences can be handled within 0.7 s.

关 键 词：Human-robot interaction SEMANTIC PARSING program synthesis intelligent robotic systems natural language unders-tanding 

分 类 号：TP[自动化与计算机技术]