Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning

Tan-Hanh Pham, Godwyll Aikins, Tri Truong +1

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we addr…