Deep Reinforcement Learning Controller Design for Unmanned Aerial Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A Proximal Policy Optimization agent was trained to learn quadrotor dynamics, successfully selecting control outputs to stabilize the drone and track complex trajectories. The agent was trained to mimic a minimum snap trajectory. The UAV closely followed the path, maintaining desired speeds of 3.56 body lengths/second, and remaining within 0.5m of the path, in wind conditions up to 20 mph. The agent was also validated on other complex trajectories, still closely tracking them regardless of the path it was trained on. Compared to PID controllers, the RL controller had a faster response time, converging to the desired path quicker. PID tuning is high maintenance and is limited by linearization around hover state. This results in instabilities and overshoots not observed in the RL controller, as well as RL learning non-linear dynamics. However, the RL controller had noisy motor output, resulting in undesirable oscillatory behaviour not observed in PID.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it