What am I touching? Learning to classify terrain via haptic sensing

Abstract

Mobile robots are becoming very popular in real-world outdoors applications, where there are many challenges in robot control and perception. One of the most critical problems is to characterise the terrain traversed by the robot. This knowledge is indispensable for optimal terrain negotiation. Currently, most approaches are performing terrain classification from vision, but there is not enough research on terrain identification from a direct interaction of the robot with the environment. In our work, we proposed new methods for classification of force/torque data from an interaction of the legged robot foot with the ground, gathered during the walking process. We provided machine learning methods for terrain classification from raw force/torque signals for which we achieved 93% accuracy on a challenging dataset with 160 minutes of recorded fixed-length steps. We also worked on a dataset where the assumption of a fixed-length step is not valid. In this case, the final result is around 80% of accuracy. The most important fact is that the data in both cases was recorded while the robot was walking, no particular movements or controlled environment were needed. Additionally, we also proposed a clustering method which allows us to learn about the class membership based on the recorded data only, without any human supervision.