Bibliographic record
Abstract
In legged mobile robotics the most common approach is to design fully actuated legs with several degrees of freedom (DOF) in order to successfully navigate through rough terrains. However, simpler leg architectures with as few as one-DOF have been developed in the past to achieve the very same goal. The ability of these simpler legs to traverse uneven terrains is arguably limited with respect to multi-DOF designs, but in some applications the reduction of the DOF and hence, of the number of actuators, as well as the simplicity of the associated control could be a great advantage and the decisive argument. In this paper, the authors propose a novel one-DOF robotic leg that has been specially designed to achieve the greatest robustness possible with respect to the difficult terrains it has to traverse. In order to do that, a method to analyze and optimize any one-DOF robotic leg with respect to its ability to overcome obstacles is proposed here. This method is based on a simple and efficient novel technique to generate synthetic terrains combined with a simulation algorithm estimating the traversability of the particular one-DOF leg design under scrutiny. To illustrate the generality of the proposed method, it is used to design both an optimal leg with the architecture presented here for the first time and also, one with the most common one-DOF leg architecture found in the literature.
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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 | 0.000 |
| 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) | 0.001 | 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".