# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 2 Literature Review
In finance, it aids fraud detection and risk assessment, leading to more secure transactions and investment decisions [25; 26]. In transportation, machine learning enables autonomous vehicles to navigate complex environments and optimize traffic flow for improved efficiency [27; 28; 29]. Additionally, machine learning has revolutionized agriculture, enabling precision farming techniques that optimize resource allocation and crop yield prediction [30; 31; 32; 33]. Overall, machine learning's ability to learn from data and make intelligent predictions has greatly enhanced fault-tolerant control strategies for robotic systems, offering improved accuracyand adaptability in fault detection, isolation, and accommodation. These methods can potentially identify and respond to a wider range of faults, including those not anticipated during the design phase, and they can continuously improve their performance over time [7]. Additionally, machine learning approaches can better handle the high dimensionality and uncertainty inherent in many modern control systems, making them particularly well-suited for applications in areas such as autonomous vehicles, robotics, and advanced manufacturing [7]. Researchers in [34] demonstrated the use of radial basis neural networks for detecting faults in robotic manipulators, achieving high accuracy by training the network on data from normal and faulty operations. In their work, Ref. [35] proposed a fault identification method that utilizes multiple source domains to enhance diagnostic accuracy in real-world scenarios. The method effectively learns and transfers generalized diagnostic knowledge from these diverse sources, improving the model's ability to identify faults in new, unseen scenarios. Supervised and unsupervised ML methods have primarily been used for fault detection, diagnosis, and isolation in robotics. Reinforcement learning is used for developing adaptive control policies that can respond to faults in real time. However, RL has emerged as a powerful tool for developing adaptive control policies that can respond to faults in real time. RL involves training an agent to make decisions by rewarding desired behaviors and penalizing undesired ones, making it particularly suitable for fault-tolerant control. Researchers compared an RL-based fault-tolerant controller with a model predictive controller (MPC) on a C-130 aircraft fuel tank model. They aimed to test the controllers' adaptability to evolving system changes during operation. Their experiments revealed that the RL-based controller performed more robustly than the MPC under various challenging conditions, including faults, partially observable system models, and fluctuating sensor noise levels [36]. Recent research has demonstrated the effectiveness of RL in various robotic applications. For instance, in [37], researchers proposed an adaptive curriculum RL algorithm with dynamics randomization to train a quadruped robot to adapt to random actuator failures. Similarly, [PERSON] et al. [38] presented a model-free adaptive fault-tolerant control algorithm based on RL for a multi-joint Baxter robot. Their approach used parameter estimation and neural networks to identify and compensate for actuator faults and spring interference, thereby improving the robot's tracking performance. RL has also shown promise in optimizing control parameters in the presence of faults. In [11], researchers developed an innovative method to optimize proportional-integral control coefficients specifically for motor position control. This RL-based approach demonstrated superior performance, computational efficiency, and user-friendly implementation compared to the traditional Ziegler-Nichols method, making it accessible even to nonexperts. Expanding the application of RL to aerial robotics, researchers in [39] developed a fault-tolerant control system for UAV landing on a moving target.