Worst-Case Execution Time Analysis of Real-Time Robotic Algorithms Using Reinforcement Learning
Bibliographic record
Abstract
This study investigates the application of Reinforce-ment Learning (RL) for estimating the Worst-Case Execution Time (WCET) of real-time robotic algorithms, crucial for ensuring reliable robotic navigation and interaction. The research eval-uates these computational techniques through two industrial case studies: (1) collision detection among two mobile cylinders and a stationary box; and (2) a six-degree-of-freedom (6-DOF) robotic model executing complex tasks. By leveraging RL, the study explores extensive input spaces to identify scenarios approaching the WCET within a controlled simulation environment. Through an experimental evaluation, we compare RL against Genetic Algorithms (GA) and random search approaches. The results demonstrate that RL outperforms both GA and random search. This research highlights the potential of RL to provide more accurate and reliable W CET estimations, thereby enhancing the safety and efficiency of real-time robotic systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".