Online unsupervised terrain classification for a compliant tensegrity robot using a mixture of echo state networks

Truly autonomous robots require the capacity to recognise their surroundings by interpreting their sensorimotor stream. We present an online learning algorithm for training a mixture of echo state network experts that can segment a compliant robot's sensorimotor stream. Our method follows a probabilistic approach, using a hidden Markov model to model the switching dynamics between the experts. The algorithm's performance is evaluated on an unsupervised terrain classification problem using a compliant, underactuated, six-strut tensegrity robot. The results show that our model captures the influence of terrain-robot interactions on the robot's complex dynamics and correctly segments the sensorimotor stream. We demonstrate that the activity pattern of the experts can be used to train a highly compliant robot to distinguish between different environments using only noisy internal sensors.