Learning from Accelerometer Data on a Legged Robot

Robot calibration for an environment is a tedious task that usually involves extensive, if not total, human intervention. However, robots have sensing mechanisms, in particular accelerometers, which could in principle be used to detect specific environmental states. In this paper, we contribute several approaches for robots to detect their state using accelerometer data. In particular, we use accelerometer data from a four-legged AIBO robot. We present a surface detector that identifies the surface under the robot as it walks using a decision tree. We present the features used for this classification. Additionally, since AIBO robots can easily become entangled on obstacles or other robots in multi-robot environments, such as robot soccer, we contribute results that show effective detection of robot state, again based on accelerometer data. Finally, we examine a third, more challenging problem: predicting gait velocity from accelerometer data. We use a knearest neighbor approach with a library of labeled accelerometer data for velocity prediction. We show that while this prediction is complex, accelerometer data can still be correlated with velocity. Our work, as reported in this paper, demonstrates the general use of robot accelerometer data for automatically detecting robot or environment state without tedious manual calibration.