Micro-Bristle Robot Design Via Different Surrogate Model Optimization Methods
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
In this paper, we optimize the locomotion speed of a micro-bristle robot using three surrogate model optimization methods: Kriging method, Bayesian method, and Deep Neural Network. Moreover, the current most popular optimization algorithm in the micro-robot optimization field, the genetic algorithm, is used as the baseline method for comparison. The four methods’ performances are tested in MATLAB, during which a state-of-art dynamic model is used. Then we 3D print the robot designs obtained from these methods and test these robot designs’ real performances. This is the first time that surrogate model optimization methods are applied on micro-robot design field. The MATLAB optimization results and the robot experimental results show that applying proper surrogate model optimization methods, especially Bayesian method will be able to obtain a satisfying robot design 5-6 times faster than the time spent by genetic algorithm. The paper provides an efficient guidance on micro-robot optimization field.
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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