One-shot learning in the road sign problem

Abstract

In this work, a one-shot learning solution to the t-maze road sign problem is presented. This problem consists in taking the correct turning decision at a bifurcation after seeing a light signal some time steps before. The recently proposed Echo State Incremental Gaussian Mixture Network (ESIGMN) is used in order to learn the correct behavior after a single scan through a single training example (and its mirrored version) generated by a simple reactive controller. Experiments with different time delays between the signal and the decision point are performed, and the ESIGMN is shown to solve the problem while achieving good performance. This one-shot ability can be useful for online learning in robotics, since the robot can learn with minimum interaction with the environment.