# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 2 Literature Review  
The increasing complexity and autonomy of robotic systems necessitate robust FTC strategies to ensure reliable operation, even in the presence of faults [5]. FTC encompasses strategies and algorithms that enable robotic systems to adapt to faults and continue operation, potentially with degraded performance, rather than experiencing catastrophic failure [12]. Faults can arise from various sources within a robotic system. Actuator faults, including partial or complete loss of effectiveness, stuck actuators, and total actuator failures, can significantly impair a robot's ability to execute desired movements and interact with its environment [13]. Sensor faults, ranging from bias, noise, and drift to complete sensor failures, can compromise a robot's perception of its surroundings, leading to inaccurate localization and decision making [14]. Structural faults, such as physical damage or degradation to robot components, can induce changes in dynamic behavior, affecting stability and control performance [15]. The core objective of FTC is to detect, isolate, and accommodate these faults to maintain overall system stability, performance, and safety. Broadly, FTC approaches are classified into two main categories. Passive FTC methods leverage robust control techniques to design controllers that are inherently tolerant to a pre-defined range of faults [16]. While not requiring explicit fault detection and isolation (FDI), their fault tolerance capacity is often limited. Active FTC methods, on the other hand, hinge on real-time FDI to accurately identify and isolate faults [3]. Based on the detected fault information, the controller is reconfigured or adapted online to preserve stability and achieve the desired performance under the given fault conditions. FTC strategies have been successfully applied to a wide range of robotic systems, demonstrating their versatility in mitigating the impact of various fault types. [PERSON] et al. [17] proposed an innovative incremental nonlinear FTC method for quadcopters experiencing a catastrophic failure: the complete loss of two opposing rotors. This critical scenario typically renders such aerial vehicles inoperable. However, by implementing this advanced FTC strategy, the quadcopter could maintain stable flight and control, albeit with reduced maneuverability, preventing otherwise catastrophic failure. [PERSON] et al. [18] proposed an innovative FTC approach that simultaneously addressed both actuator and sensor faults. Their method employed nonlinear backstepping control coupled with friction compensation, enhancing the manipulator's ability to maintain precise movements and positioning even when faced with sensor inaccuracies or actuator inefficiencies. Researchers tackled the complex challenge of controlling uncrewed underwater vehicles in the presence of multiple system uncertainties and disturbances. They developed a novel sensor-active FTC scheme that achieved trajectory tracking without relying on linear and angular velocity measurements [19]. Traditional fault-tolerant control systems have several limitations, including their reliance on accurate system models, their difficulty in handling complex nonlinear systems, and their limited adaptability to unforeseen faults or changing conditions. These systems often struggle with uncertainty and may fail when encountering scenarios not explicitly accounted for in their design [20]. In contrast, learning-based methods offer a more robust and flexible approach to fault-tolerant control. By leveraging data-driven techniques, learning-based FTC can adapt to new situations, learn from experience, and handle complex, nonlinear systems more effectively [21]. Machine learning (ML) has significantly impacted various areas, greatly improving efficiency and capabilities. In healthcare, ML has enhanced diagnostic accuracy and treatment personalization by analyzing vast patient data and medical images [22; 23; 24].