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Learning terrain types with the Pitman-Yor process mixtures of Gaussians for a legged robot

One of the major goals for mobile robots is to be able to traverse any kind of terrains. A possible way to achieve this goal is by the use of legged robots, as they have increased mobility. However, this would require them to be able to modify their gaits, based on the identification of the terrain that they are currently traversing. In this paper, we introduce a number of novel methods to address this issue of autonomous terrain classification and clustering, based on tactile data collected with a walking robot. The proposed learning methods are based on the Pitman-Yor process mixture of Gaussians, a Bayesian nonparametric prior, well-suited for density estimation. This model is initially used to learn the non-Gaussian distribution of the features produced from proprioceptive (force/torque) signals from the legs, registered during the interaction of one robot foot with a terrain. Then, we exploit its capacity on clustering and discovering structures in the data to identify terrains in the feature space. Experiments were conducted on a six-legged robot, thus demonstrating the applicability of the Pitman-Yor process mixture of Gaussians for terrain identification. In particular, we obtained a classification success rate of 82% and 51% accuracy, with our supervised learning and unsupervised learning approach respectively.