Machine learning comparison for step decision making of a bipedal robot

This paper presents the results of several machine learning techniques for step decision in a bipedal robot. The custom developed bipedal robot does not utilize electric motors as actuators and as a result has the disadvantage of imprecise movements. The robot is inherently unstable and maintain its stability by making steps. The classifiers had to learn when and which leg must be moved in order to maintain stability and locomotion. Methods like: Decision tree, Linear/Quadratic Discriminant, SVM, KNN and Neural Networks were trained. The results of their performance/accuracy are noted.