A service robot accepting verbal commands from a human operator is likely to encounter requests that reference objects not currently represented in its knowledge base. In domestic or office settings, the construction of a complete knowledge base would be cumbersome and unlikely to succeed in most real-world deployments. The world that such a robot operates in is thus "open: in the sense that some objects that it must act on in the real world are not described in its internal representation. However, when an operator gives a command referencing an object that the robot has not yet observed ( and thus not incorporated into its knowledge base), we can think of the object as being hypothetical to the robot. This paper presents a novel method for closing the robot's world model for planning purposes by introducing hypothetical objects into the robot's knowledge base, reasoning about these hypothetical objects, and acting on these hypotheses in the real world. We use our implementation of this method on a domestic service robot as an illustrative demonstration to explore how it works in practice.

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In Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019), Berkeley, CA, USA, July 2019.

@inproceedings{ICAPS19-Jiang, title={Open-World Reasoning for Service Robots}, author={Yuqian Jiang and Nick Walker and Justin Hart and Peter Stone}, booktitle={Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019)}, month={July}, address={Berkeley, CA, USA}, url="http://www.cs.utexas.edu/users/ai-lab?ICAPS19-Jiang", year={2019} }