Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning
Hanqing Zhao,Xue Liu,Gregory Dudek,Hanqing Zhao,Xue Liu,Gregory Dudek
We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i.e. fault detection)....
