An adaptive spiking neural controller for flapping insect-scale robots
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
Insect-scale flapping robots are challenging to stabilize due to their fast dynamics, unmodeled parameter variations, and the periodic nature of their control input. Effective controller designs must tolerate wing asymmetries that occur due to manufacturing errors and react quickly to stabilize the fast unstable modes of the system. Additionally, they should have minimal power requirements to fit within the tightly constrained power budget associated with insect-scale flying robots. Adaptive control methods are capable of learning online to account for uncertain physical parameters and other model uncertainties, and can thus improve system performance over time. In this work, a spiking neural network is used to stabilize hovering of an insect-scale robot in the presence of unknown parameter variations. The controller is shown to adapt rapidly during a simulated flight test and requires a total of only 800 neurons, allowing it to be implemented with minimal power requirements.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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