Modern robotic architectures are equipped with sensors enabling a deep analysis of the environment. In this work, we aim at demonstrating that such perceptual information (here modeled through semantic maps) can be effectively used to enhance the language understanding capabilities of the robot. A robust lexical mapping function based on the Distributional Semantics paradigm is here proposed as a basic model of grounding language towards the environment. We show that making such information available to the underlying language understanding algorithms improves the accuracy throughout the entire interpretation process.
Cite as: Bastianelli, E., Croce, D., Basili, R., Nardi, D. (2015) Using semantic maps for robust natural language interaction with robots. Proc. Interspeech 2015, 1393-1397, doi: 10.21437/Interspeech.2015-55
@inproceedings{bastianelli15_interspeech,
author={Emanuele Bastianelli and Danilo Croce and Roberto Basili and Daniele Nardi},
title={{Using semantic maps for robust natural language interaction with robots}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={1393--1397},
doi={10.21437/Interspeech.2015-55}
}