A deep learning approach for understanding natural language commands for mobile service robots
This work addresses the problem of enabling robots to interpret natural language commands, which is incremental as it builds on existing neural network and SVM methods.
The paper tackles the challenge of understanding natural language commands for mobile service robots by modeling commands as actions and slots, using LSTM networks for action detection and slot filling, and an SVM for capability checking. It achieves superior accuracy with LSTMs and is evaluated on datasets for domestic service robots.
Using natural language to give instructions to robots is challenging, since natural language understanding is still largely an open problem. In this paper we address this problem by restricting our attention to commands modeled as one action, plus arguments (also known as slots). For action detection (also called intent detection) and slot filling various architectures of Recurrent Neural Networks and Long Short Term Memory (LSTM) networks were evaluated, having LSTMs achieved a superior accuracy. As the action requested may not fall within the robots capabilities, a Support Vector Machine(SVM) is used to determine whether it is or not. For the input of the neural networks, several word embedding algorithms were compared. Finally, to implement the system in a robot, a ROS package is created using a SMACH state machine. The proposed system is then evaluated both using well-known datasets and benchmarks in the context of domestic service robots.