## 3 Experiments and Results
### Generalization
#### 3.4.2.1 Varying Insertion Tasks  
Lastly, we evaluated the learned policy through a series of novel insertion tasks, none seen during training, to assess its generalization capabilities. These insertion tasks included challenges such as adapting to a very hard surface (high stiffness), requiring a minimal insertion force to perform the insertion, and a complex peg shape for mating the parts. The different insertion scenarios are depicted in Figure 9.  
For each task, the learned policy was executed 20 times from random initial positions and assuming perfect estimation of the goal position. Table 5 shows the success rates of the learned policies in these novel tasks, along with the desired insertion force set for each task. As the insertion force was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method/Stiffness** & **High** & **Medium** & **Low** \\ \hline
**Scratch** & 100\% & 70\% & 40\% \\
**Sim2 real** & 95\% & 100\% & 100\% \\
**Ours** & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Success rate of a 3D-printed-cuboid insertion task with different degrees of contact stiffness.  
Figure 8: High, medium, and low-stiffness environments (left to right).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Estimation Error/Success Rate**} \\ \hline \hline  & \multicolumn{4}{c}{**Position**} & \multicolumn{4}{c}{**Orientation**} \\ \hline
**Method** & **1 mm** & **2 mm** & **3 mm** & **4 mm** & **5 mm** & **1\({}^{\circ}\)** & **2\({}^{\circ}\)** & **3\({}^{\circ}\)** & **4\({}^{\circ}\)** & **5\({}^{\circ}\)** & **Random** \\ \hline
**Scratch** & 90\% & 90\% & 70\% & 55\% & 35\% & 100\% & 90\% & 80\% & 80\% & 50\% & 80\% \\ \hline
**Sim2 real** & 90\% & 85\% & 75\% & 60\% & 40\% & 100\% & 90\% & 80\% & 80\% & 30\% & 75\% \\ \hline
**Ours** & **100\%** & **100\%** & **95\%** & **65\%** & **60\%** & **100\%** & **100\%** & **100\%** & **100\%** & **90\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of learning from scratch, straightforward sim2 real, and sim2 real + retraining (ours) with different degrees of goal-position uncertainty error. Test performed during 3D-printed cuboid-peg insertion task.  
defined as a policy input, we could define a specific desired insertion force for each task. Even though the policy was only trained by using the simpler cuboid-peg insertion task, mainly in the simulation and shortly after refined in a real robot with a 3D-printed version of the same task, the learned policy achieved a high success rate in novel and complex insertion tasks.  
Compared to the cuboid-peg insertion task, in these novel insertion tasks, the peg was more likely to become stuck during the task's search phase, as the surrounding surface near the hole was not smooth and may have had crevices. The extra challenges were not present during the training phase, which reduced the capability of the learned policy to react in an appropriate way. The insertion task of the LAN port was the most challenging for the policy due to the complex shape of the LAN cable endpoint. If just one corner of the LAN adapter was stuck, the insertion could not be completed even if a large force was applied.  
Additionally, we tested the policy on different insertion planes for the electric outlet and the LAN port tasks. In both cases, the success rate was similar due to training with the randomized insertion planes. However, the policy was slightly better with insertions on the y axis plane due to retraining (on the real robot) only being done on this axis.