Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning

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In this paper, we propose a novel integrated task planning system for service robots in domestic domains. Given open-ended high-level user instructions in natural language, robots need to generate a plan, i.e., a sequence of low-level executable actions, to complete the required tasks. To address this, we exploit the knowledge on semantic roles of common verbs defined in semantic dictionaries such as FrameNet and integrate it with Answer Set Programming --- a task planning framework with both representation language and solvers. In the experiments, we evaluated our approach using common benchmarks on service tasks and showed that it can successfully handle much more tasks than the state-of-the-art solution. Notably, we deployed the proposed planning system on our service robot for the annual RoboCup@Home competitions and achieved very encouraging results.

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 address = {Melbourne, Australia},
 author = {Dongcai Lu and Yi Zhou and Feng Wu and Zhao Zhang and Xiaoping Chen},
 booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI)},
 doi = {10.24963/ijcai.2017/609},
 month = {August},
 pages = {4361-4367},
 title = {Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning},
 year = {2017}