Hao Li, Yu-Ping Wang and Tai-Jiang Mu. Synthesizing Robot Programs with Interactive Tutor Mode. International Journal of Automation and Computing, vol. 16, no. 4, pp. 462-474, 2019. DOI: 10.1007/s11633-018-1154-7
Citation: Hao Li, Yu-Ping Wang and Tai-Jiang Mu. Synthesizing Robot Programs with Interactive Tutor Mode. International Journal of Automation and Computing, vol. 16, no. 4, pp. 462-474, 2019. DOI: 10.1007/s11633-018-1154-7

Synthesizing Robot Programs with Interactive Tutor Mode

  • 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.
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