An Application of Model-Free Reinforcement Learning to the Control of Aerial Vehicles With Slung Payloads
Why this work is in the frame
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Bibliographic record
Abstract
Model-free control has gained in popularity in recent years for its adaptive, data-driven solutions to complex control objectives. This paper discusses the application of model-free reinforcement learning (RL) to the control of multibody aerial vehicles. First, a summary of model-free RL as it applies to robotic systems is presented. Then, a model-free RL controller is demonstrated, whereby an actor-critic algorithm is used to stabilize the swing of a slung payload carried by an autonomous quadcopter aerial vehicle, utilizing the latter’s full continuous action-space. Its effectiveness and adaptability are first demonstrated in simulation, and then validated on an experimental testbed. The algorithm is shown to be computationally efficient enough to adapt to the experimental testbed in real-time, and to work within the framework of widely-used autopilot software ArduPilot.
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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 | 0.001 | 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