# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 5 Results and Discussion
### RL Training Results
In the no-fault scenario, the RL algorithm demonstrated a high success rate of 98.00% with an average completion time of 3.54 s. This served as the baseline for our experiments, indicating the algorithm's effectiveness in completing the task efficiently when there were no joint malfunctions. In addition, when a joint was permanently broken, the success rate remained high at 96.00%, although the average completion time increased to 4.62 s. This increase suggests that while the algorithm compensated for the loss of functionality by adjusting its strategy, it did so at the cost of increased time. In the case of an intermittently functioning joint, the success rate was also 96.00%, with a slightly higher average completion time of 3.77 s compared to the no-fault scenario. This indicates that the algorithm could quickly adapt to intermittent faults and maintain efficiency without significant delays. When the joint functioned only during the first half of the task, the success rate remained at 96.00%, with an average completion time of 4.11 s. This result implies that the RL algorithm effectively utilized the functional period of the joint to complete the task, demonstrating its ability to optimize performance under partial functionality. However, the scenario where the joint worked only in the second half presented the most significant challenge. The success rate dropped to 82.00%, and the average completion time increased substantially to 8.02 s. The reduced success rate and longer completion time indicate that the algorithm struggled more when the joint only functioned in the latter part of the task. This difficulty likely arose from the initial lack of joint functionality in the first half, requiring the algorithm to employ more complex strategies to compensate and leading to longer task completion times and lower success rates. Figure 3 shows two cases: one where the robot successfully opened the cabinet and the other where the robot failed to open the cabinet. Generally, our framework demonstrated strong adaptability and robustness across different fault scenarios, maintaining high success rates in most cases. The completion time varied depending on the type and timing of the joint malfunction, with intermittent faults and partial functionality scenarios resulting in moderate increases in time. The scenario where the joint worked only in the second half posed the greatest challenge, highlighting an area for potential improvement in the algorithm's adaptability to late-functioning components. However, these results underscore the effectiveness of the RL algorithm in handling joint malfunctions, ensuring reliable task completion even under adverse conditions. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Fault Scenario** & **Success Rate (\%)** & **Average Completion Time (s)** \\ \hline No Fault & 98.00 & 3.54 \\ Permanently Broken Joint & 96.00 & 4.62 \\ Intermittently Functioning Joint & 96.00 & 3.77 \\ Joint Works First Half & 96.00 & 4.11 \\ Joint Works Second Half & 82.00 & 8.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Task completion performance under different fault scenarios.