Analysis and Optimization of One-Degree of Freedom Robotic Legs
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
Almost all walking robots are composed of two or more multi-degrees-of-freedom (DOFs) legs which give them a good ability to traverse obstacles. Nevertheless, their speed and efficiency when traversing rough terrains is, in most cases, arguably limited. Additionally, they have the disadvantage of a generally lower reliability. The design of robust and efficient 1-DOF leg is, on the other hand, a complex process. In this paper, a method to analyze and optimize 1-DOF robotic legs is proposed. The results of a virtual simulation are used in combination with some performance indices to optimize the geometric parameters of 1-DOF legs. Finally, the results of the simulation and the actual walking performance of a prototype using four legs with the computed optimal parameters are presented and compared with the simulator results. The validation of the simulation model and the optimization method proposed in this paper represents the main contribution of this work.
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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