Worm-like robotic systems: Generation, analysis and shift of gaits using adaptive control
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
The starting point of this work is a biologically inspired model of a worm-like locomotion system (WLLS). The mechanical model comprises discrete mass points connected by viscoelastic force actuators. Ground contact is constituted by ideal spikes which act as constraint forces, preventing backward motion for each mass point equipped with them. The distances between each two consecutive mass points are changed by an adaptive controller in order to track a reference trajectory. In combination with the ground contact via spikes, this results in a (undulatory) locomotion of the system.After presenting the aforementioned model and the adaptive controller, the construction of specific reference functions, which result in certain gaits, is described. For this purpose an existing algorithm is used; it allows for defining the number and succession of the active spikes as well as the resulting velocity. In the following gait examination, simulations for worm systems with four mass points are carried out to find a selection of those gaits most suitable in terms of actuator and spikes load. Prior to implementing the automatic gait change, simulations are carried out to determine the criteria for shifting: actuator and spike forces. With those criteria, the choice of the optimal gait depends on both locomotion speed and ground inclination. An approximation of the forces mentioned before enables a formulation of inclination-dependent speed intervals. This leads to a combination of speed adjustment and gait change that enables optimal crawling for predefined limits of actuator or spike forces.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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