A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

TL;DR

This paper proposes a multi-layer LSTM neural network with attention for semantic parsing of vocal commands to service robots, aiming to improve natural language understanding in human-robot interaction.

Contribution

It introduces a novel multi-layer LSTM-based approach with attention for semantic parsing in robot command understanding, trained on a dedicated corpus.

Findings

Preliminary results show promising parsing accuracy.

The approach outperforms some previous methods.

The system demonstrates potential for real-world robot interaction.

Abstract

As the first robotic platforms slowly approach our everyday life, we can imagine a near future where service robots will be easily accessible by non-expert users through vocal interfaces. The capability of managing natural language would indeed speed up the process of integrating such platform in the ordinary life. Semantic parsing is a fundamental task of the Natural Language Understanding process, as it allows extracting the meaning of a user utterance to be used by a machine. In this paper, we present a preliminary study to semantically parse user vocal commands for a House Service robot, using a multi-layer Long-Short Term Memory neural network with attention mechanism. The system is trained on the Human Robot Interaction Corpus, and it is preliminarily compared with previous approaches.