Abstract
We present an implemented robotic system that learns elements of its semantic and episodic memory through language interaction with its human users. This human-like learning can happen because the robot can extract, represent and reason over the meaning of the user’s natural language utterances. The application domain is collaborative assembly of flatpack furniture. This work facilitates a bi-directional grounding of implicit robotic skills in explicit ontological and episodic knowledge and of ontological symbols in the real-world actions by the robot. In so doing, this work provides an example of successful integration of robotic and cognitive architectures.
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Acknowledgements
This work was supported in part by Grant N00014-17-1-221 from the U.S. Office of Naval Research. Any opinions or findings expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research.
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Nirenburg, S. et al. (2018). Toward Human-Like Robot Learning. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_8
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DOI: https://doi.org/10.1007/978-3-319-91947-8_8
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