Bibliographic record
Abstract
Although effective low-level control configurations of quadrotors are already known, the tuning of such controllers requires extensive expert knowledge which can impede their design and deployment. Considering the growing demand for quadrotors in different environments, the importance of an automated approach to designing the controller cannot be neglected. For this purpose, recently, a successful implementation of a model-based reinforcement learning technique was demonstrated by training a neural network using only flight data. In this paper, as an alternative to the neural network approach, we employ a structured model parameterized by a set of bases to identify the governing dynamics of quadrotors. The model accompanied by a value function defined in the product space of the bases leads to an analytical update rule for the controller that can be effectively solved by ODE solvers. The runtime results confirm that the controller together with a recursive least squares identifier can be used as a lightweight framework for learning to stabilize an unknown quadrotor at a given position. In the simulation results, a nonlinear model of the quadrotor is exploited that replaces the real unknown quadrotor. The flight data and 3D graphical simulation are generated to verify the presented learning approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".