Semantic Role Labelling for Robot Instructions using Echo State Networks

Overview & Synopsis

To control a robot in a real-world robot scenario, a real-time parser is needed to create semantic representations from natural language which can be interpreted. The parser should be able to create the hierarchical tree-like representations without consulting external systems to show its learning capabilities. We propose an efficient Echo State Network-based parser for robotic commands and only relies on the training data. The system generates a single semantic tree structure in real-time which can be executed by a robot arm manipulating objects. Four of six other approaches, which in most cases generate multiple trees and select one of them as the solution, were outperformed with 64.2% tree accuracy on difficult unseen natural language (74.1% under best conditions) on the same dataset.