Online unsupervised terrain classification for a compliant tensegrity robot using a mixture of echo state networks
Jeroen Burms, Ken Caluwaerts, Joni Dambre
Abstract
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.
BibTeX
@inproceedings{icra2015_onlineunsupervis,
title = {Online unsupervised terrain classification for a compliant tensegrity robot using a mixture of echo state networks},
author = {Jeroen Burms and Ken Caluwaerts and Joni Dambre},
booktitle = {ICRA 2015},
year = {2015}
}