Recursive neural network based semantic navigation of an autonomous mobile robot through understanding human verbal instructions

Abstract

In this research, we aim to implement the ability to navigate through unknown environments according to some given instructions on mobile robots. We proposed a Recursive Neural Network model, which takes the user instructions and data acquired by laser range finder as input, and outputs control velocities to make the mobile robot navigate in an unknown indoor environment. Instructions are using commonly verbal expressions, and instead of establishing models to represent the semantic meanings, we use the concept of word vectors to feed the information directly into the neural network models. Using human-controlled navigating records as training data, robots learn how to execute navigations according to various of different instructions. Experiments were conducted under both simulation and real world environments, and results show that the robot can successfully navigate to the goal positions without having any prior knowledge about the environments.