Bibliographic record
Abstract
The study of synthetic nervous systems is an emerging field within biomimetic robotics as an alternative to more classic control techniques. As the modeling of these nervous systems becomes more accurate, it is important to note that the nervous system and physical system co-evolved and continue to operate in an interdependent fashion. Many legged robots, including the existing quadruped in the AARL, have leg systems that may have similar geometrical properties to that of a mammal, but have significantly different dynamic properties. This paper presents a method for designing a limb so that the passive dynamics more accurately represent that of mammalian limbs. The desired limb dynamics were obtained by scaling kinematic rat leg data, and a gray-box optimization method was used to determine appropriate spring and damper properties, modeling the limb as a three-link pendulum with a spring-damper system at each joint. The new leg was designed with minimal changes to the current prototype, with the implementation of a modular spring and damper set, which allows the leg to achieve more biomimetic passive dynamics. This leg was built with the intent of comparing SNS control methods on legs with different dynamic scales (i.e. inertial vs overdamped). Future improvements to the spring/damper implementation will include increasing the modularity of the mechanical design in order to more easily change the leg dynamic properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".