Learning to assess terrain from human demonstration using an introspective Gaussian-process classifier

This paper presents an approach to learning robot terrain assessment from human demonstration. An operator drives a robot for a short period of time, supervising the gathering of traversable and untraversable terrain data. After this initial training period, the robot can then predict the traversability of new terrain based on its experiences. We improve on current methods in two ways: first, we maintain a richer (higher-dimensional) representation of the terrain that is better able to distinguish between different training examples. Second, we use a Gaussian-process classifier for terrain assessment due to its superior introspective abilities (leading to better uncertainty estimates) when compared to other classifier methods in the literature. Our method is tested on real data and shown to outperform current methods both in classification accuracy and uncertainty estimation.