## 4 Results
### Grid-Based Testing Scenarios  
To exactly examine the robustness and the generalizability of the trained policy, we perform a systematic testing in an arena distinct from training. The test environment differs from the training environments in terms of shape, texture, and lighting conditions to enable the analysis of the methods generalization potential. We set the initial pose of the (2D location and orientation) in an exhaustive grid-based manner and checked the success probability of each initial position. The exhaustive testing features a \(5\) m \(\times\) \(5\) m grid, partitioned into \(0.5\) m grid-cells, centered in front of the dolly. The grid thus represents \(11\times 11\) initial positions for the mobile robot. Figure 6 illustrates a schematic representation of the testing scenario. Furthermore, we test each initial positions with eight different initial robot orientations, given by the following list: \(\{0^{\circ},\pm 45^{\circ},\pm 90^{\circ},\pm 135^{\circ},180^{\circ}\}\). For simplicity, the orientation of \(0^{\circ}\) can be approximately understood as the case where the robot is facing straight towards the goal and \(180^{\circ}\) corresponds to facing backwards from the goal. Please refer to Appendix C for details. The effect of partial observability aggregates with the  
Figure 4: Comparison of (**a**) groundtruth image sequence and (**b**) reconstructed image sequence from auto-encoders. increasing angle. Each combination of position and rotation is tested nine times to obtain an averaged estimation of the success probability for each configuration (x, y, orientation), which corresponds to the total number of \(11\times 11\times 8\times 9=8712\) runs for each ablation variant. Figure 6 visualizes the test-scenario graphically. It is noteworthy that the testing case features wider initial AVG orientation, lying between the interval of \([-180^{\circ},180^{\circ}]\), which extrapolates the defined one of \([-90^{\circ},90^{\circ}]\) in the training phase. In this way, one can also examine the performance of learned policy under the effect of partial observability. The best run among the three runs of _NavACL p.t._ is illustrated in Figure 7a. It is manifest that the agent scores approximately 100% probability to reach the dolly from a favorable initial starting position. The term 'favorable' stands for a relatively small initial robot orientation from the target dolly, e.g., \(0^{\circ},\pm 45^{\circ},-90^{\circ}\). These cases suffer minimally from partial observation and the agent can reach goal mostly with proper turning and maneuvering. With aforementioned favorable initial rotation angle, the mobile agent mostly succeeds in navigating to the goal, except for left/right upper corner cases, i.e., the region near \(-3\) m in x-axis and \(-2\) m in y-axis for rotation angle \(-90^{\circ}\). This result is reasonable as such corner case is deemed as difficult start position as the robot cannot capture the target from the RGB camera and the mobile robot has to make a series of  
Figure 5: Learning curves of the three variants. (**a**) The episodic return, (**b**) The docking success probability per episode. These two statistics are presented as a moving-average over 500 episodes, where the solid line and shaded area illustrates, respectively, the mean and the variance over three runs. Figure 6: (**a**) Schematic representation of the grid-based test-scenario. The coordinate system to which the test-scenario refers is shown in red. The fixed dolly position is marked with “D”. The blue grid represents the test zone divided into \(11\times 11\) positions. The grid and the dolly are scaled up for this illustration to improve visibility. (**b**) Graphical illustration of a \(0^{\circ}\) rotation test, conducted in the simulated testing-environment. adjusting maneuvers to reach upright underneath the dolly, similar to parking the cars to a narrow slot, hence resulting in a sub-optimal policy.