A forward model for an active tactile sensor using Echo State Networks

Abstract

Here, we introduce a forward model designed for predicting the expected reading of a bionic tactile sensor (antenna) mounted onto a wheeled robot. The model was used to distinguish self-generated stimulation from true tactile events to the antenna. An Echo State Network (ESN), a special type of recurrent neural network which is suitable for chaotic time series prediction, is used to implement the forward model. Inputs to the ESN are the motor command which sets the position of the antenna, and a local proprioceptive signal which measures the acceleration of the robot platform. The model can successfully be used to detect a tactile contact on the antenna while the robot is moving along a path with obstacles. Such forward models are good candidates to be used in neural yet simple way to eliminate self-stimulation of sensors of other modalities due to ego-motion.