## 4 Results and Discussion
### Evaluation of Self-Learning Mudskipper-Inspired Crawling Method (SLMIC)
#### 4.2.2 Comparison of Experimental Results Obtained Using Previous Control Method and SLMIC  
Because SLMIC exhibited the best performance in the simulation, we decided to conduct the experiment involving the actual robot using only with SLMIC. We compared the performance of SLMIC with the previous controller, that is, trotting gait (TG). The test cases were the same as those in the experiments conducted in Section 4.2.1.  
1. case A: The results of this test case clearly show that with SLMIC the robot could recover itself to reach the goal, as shown in Figure 10(b). Compared to the ground truth (in Figure 10(a)), the robot programmed with SLMIC almost traveled the same distance as the healthy robot. By contrast,  
Figure 19: Comparison of simulation results obtained using EA, EAD, MUD and SLMIC: (**a**) ground truth of healthy robot walking with trotting gait; (**b**) one leg lost (case A); (**c**) two adjacent legs lost (case B); (**d**) two diagonal legs lost (case C); (**e**) two adjacent legs and one limb lost (case D).  
the robot could not move well when the previous control method (trotting gait controller) was employed. It caused the robot to move back and fourth around a single point in a certain working area.
2. case B: In this case, SLMIC with the damaged robot achieved the same result as the healthy robot. However, the robot moved slightly towards the right part of the working space. By contrast, the robot with two adjacent legs-lost and programmed by the previous method could not perform well, traveling only around the starting point, as shown in Figure 20c.
3. case C: As shown in Figure 20d, with the trotting gait, the broken robot could not function properly and moved backwards during the experiment. This can be one of the reasons why the recovery method is significant for multiple-legged robots. By contrast, the proposed method provided good performance with the learning process. According to the trajectory traveled by the robot programmed with the proposed method, it moved towards the right at the beginning, but it returned to the predetermined direction with the passage of time.
4. case D: Similar to results of the simulation in the previous section, the robot with two adjacent legs and one limb lost found it difficult to achieve the same performance as the healthy robot. However, SLMIC made a big difference compared to the previous controller. Even so, it could not help the robot recover fully, but it did help the damaged robot cover more than half the distance covered by the healthy robot.  
Figure 20: Experimental results of SLMIC compared to previous controller (trotting gait): (**a**) ground truth of healthy robot walking with trotting gait; (**b**) one leg lost (case A); (**c**) two adjacent legs lost (case B); (**d**) two diagonal legs lost (case C); (**e**) two adjacent legs and one limb lost.  
After the experiments with actual robots were conducted properly, it was found that the damaged robot could practically not move when the previous controller (trotting gait in this case) was used. As a result, the recovery method that can provide an alternative solution for damaged robots is needed. With SLMIC, the proposed recovery method based on specific actions that guarantee adaptable movement owning to learning could solve most of the problems successfully, and, in one special difficult case, it recovered by approximately 50 percent. The example of robot actions with SLMIC in Figure 21 (case C) shows that the robot turned slightly to the wrong direction at the beginning, but it recovered and moved along the correct direction at the end. Finally, the experiments confirmed that the proposed method (SLMIC) is suitable for quadruped robots with a limited number of functional legs.