Robust Neurocontrol for Autonomous Dynamic Soaring
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
The flight endurance of small unmanned aerial vehicles can be significantly extended through the exploitation of naturally occurring wind phenomena. However, due to the limited computational hardware on board such aircraft and the uncertain, stochastic nature of real-world environments, there is a need for efficient and robust strategies that exhibit generalized behavior. In addressing these objectives, recent efforts have explored the use of artificial intelligence training algorithms and neural networks for the design of autonomous control schemes that exploit such wind phenomena. This study incorporates the Neuroevolution of Augmenting Topologies algorithm with domain randomization to train robust neurocontrollers that can control an aircraft along sustained traveling dynamic soaring trajectories in the presence of uncertainties and disturbances. This work presents the developed strategy for integrating robustness in neural network control systems, provides a method for quantifying and comparing robustness, and introduces an approach for identifying the network characteristics that contribute to the evolved robust behavior.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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