## 1 Introduction  
In recent years, legged robots have been widely utilized in several applications due to the fact that legged robots are more flexible than wheel-based robots in terms of mobility and energy efficiency [1]. Despite the agility and complex maneuverability of legged robots over wheeled robots, one major drawback is their inability to operate when they are damaged. In general, legged robots can function properly with predesigned controllers. However, there are some failures that occur when some parts of robots are not working, such as encoder drifting, broken legs, and joint failure [2; 3]. Prior control strategies cannot be employed efficiently with the transferred models of damaged robots. Researchers have discovered and developed solutions to overcome this problem. The ability of system that can continue functioning in the presence of faults is known as fault tolerance [4]. In fault tolerant system, there are four processes to be considered which are fault detection, fault location, fault containment, and fault recovery. Fault detection is a process to determine that a fault has occurred. Fault location is a process to determine where a fault has occurred. Fault containment is a process to isolate a fault from the system. Fault recovery is a process to make the system recover from a fault. Fault-tolerant gait planning is the recovery method used with multi-legged robots after a failure has occurred and hampersits ability to walk or maintain stability [5]. According to the study in the literature, we can categorize recovery methods for damaged multi-legged robots into four main groups, namely, evolutionary-assist method, gait transition method, task-based method, and learning method [6]. [PERSON] et al. proposed an algorithm that help damaged robot walk again [7]. In their work, the robot could start the process of identifying its current model and employing an evolutionary algorithm to determine the behavior (self-learning method) that provided the best movement. They showed that a robot with the broken legs can travel forward. However, this method was not successful in all scenarios. Other similar methods have been reported by [PERSON] and [PERSON] et al. [8; 9]. They employed the evolutionary methods, such as a Genetic Algorithm (GA), to create sequential actions that can guide the robot to move forward. However, these methods are time-consuming tasks. The robots are required to execute actions repeatedly until they receive the maximum outcome. The problems include not just the time spent but also the gap between the results of simulation and real experiments. Typically, the evolutionary methods require a level of numerical processing that is tough enough to perform with the hardware available on the robot body. Therefore, the researchers used a simulation model on a computer to discover the maximum-distance-traveled actions, which is the cause of the aforementioned mismatch between the simulation and the experimental results. [PERSON] et al. presented a method that works in simulation as well as in reality [10]. Using this method, the robot can identify suitable actions in a simulation and then execute such actions on real robots. This method provided very good results in terms of moving ability and time efficiency. However, given that the method required some processing time, the same research group came up with a new idea called \"the robot that can adapt like an animal\" that was published by [PERSON] et al. [11]. They used the trial-and-error technique combined with a large search space. The robot performed many possible actions in the simulation beforehand (which lasted approximately one week) and these actions were stored in the search space to be picked up when the robot needed to adapt. By using this algorithm, the robot spent less than two minutes for recovery. Even though this method was applied successfully with a hexapod robot and manipulator systems, implementation with a quadruped robot was not reported.