A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
This addresses natural language understanding for non-expert users interacting with service robots, but it is incremental.
The paper tackled semantic parsing of vocal commands for a house service robot using a multi-layer LSTM with attention, achieving preliminary results compared to prior methods.
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.