Detecting anomalies in humanoid joint trajectories

We present a semi-supervised anomaly detection system for humanoid robots that operates on trajectories with varying lengths, resolutions, and time shifts. The proposed approach utilities optimization to extract a model from joint trajectories under normal operation and seek to identify anomalous behaviors that deviates significantly from the known model. Compared to previously proposed approaches in humanoid anomaly detection that identified only high-level faults, our approach can detect subtle defects in the robot and at the same time, is capable of generalizing to higher-level behaviors. The system is demonstrated on a simulated model of the Atlas humanoid robot, with several experimental scenarios demonstrating detection of both joint-level anomalies and behaviors such as falling.