Analyzing Adversarial Attacks against Deep Learning for Robot Navigation

The autonomous system sector continues to experiment and is still progressing every day. Currently, it affects several applications, namely robots, autonomous vehicles, planes, ships, etc. The design of an autonomous system remains a challenge despite all the associated technological development. One of such challenges is the robustness of autonomous system decision in an uncertain environment and their impact on the security of systems, users and people around. In this work, we deal with the navigation of an autonomous robot in a labyrinth room. The objective of this paper is to study the efficiency of a decision-making model, based on Deep Neural Network, for robot navigation. The problem is that, under uncertain environment, robot sensors may generate disturbed measures affecting the robot decisions. The contribution of this work is the proposal of a system validation pipeline allowing the study of its behavior faced to adversarial attacks i.e. attacks consisting in slightly disturbing the input data. In a second step, we investigate the robustness of robot decision-making by applying a defence technique such as adversarial training. In the experiment stage, our study uses a on a public robotic dataset.