Insect inspired unsupervised learning for tactic and phobic behavior enhancement in a hybrid robot

In this paper the implementation of a correlation-based navigation algorithm, based on an unsupervised learning paradigm for spiking neural networks, called Spike Timing Dependent Plasticity (STDP), is presented. The main characteristic of the learning technique implemented is that it allows the robot to learn high-level sensor features, based on a set of basic reflexes, depending on some low-level sensor inputs. The goal is to allow the robot to autonomously learn how to navigate in an unknown environment, avoiding obstacles and heading toward or avoiding the targets (on the basis of the rewarded action). This algorithm was implemented on a bio-inspired hybrid mini-robot, called TriBot. The peculiar characteristic of this robot is its mechanical structure, since it allows to join the advantages both of legs and wheels. In addition, it is equipped with a manipulator that allows to add new capabilities, like carry objects and overcome obstacles. Robot experiments are reported to demonstrate the potentiality and the effectiveness of the approach.