  Legged robots are popular candidates for missions in challenging terrains due
to the wide variety of locomotion strategies they can employ. Terrain
classification is a key enabling technology for autonomous legged robots, as it
allows the robot to harness their innate flexibility to adapt their behaviour
to the demands of their operating environment. In this paper, we show how
highly capable machine learning techniques, namely gated recurrent neural
networks, allow our target legged robot to correctly classify the terrain it
traverses in both supervised and semi-supervised fashions. Tests on a benchmark
data set shows that our time-domain classifiers are well capable of dealing
with raw and variable-length data with small amount of labels and perform to a
level far exceeding the frequency-domain classifiers. The classification
results on our own extended data set opens up a range of high-performance
behaviours that are specific to those environments. Furthermore, we show how
raw unlabelled data is used to improve significantly the classification results
in a semi-supervised model.
