Bibliographic record
Abstract
A Metamorphic Robots System is a modular self-reconfigurable robotic system composed of autonomous mobile modules in a 2D (or 3D) regular grid. The modules have limited computational capabilities, interact only with neighboring modules, and can move around adjacent modules from a cell to an empty neighboring cell under specific conditions. An important well-studied problem for these robotic systems is Motion Planning, also known as Shape Formationor Self-reconfiguration, requiring the modules to organize themselves into a pre-determined final configuration (i.e., shape); basic shapes such as the line (or chain) are especially important as they are utilized as a foundation for constructing more complicated shapes and are an initial measure for handling complicated tasks. A metamorphic robots system could offer a higher degree of reliability and robustness compared to fixed-architecture robots due to its capacity to self-repair: should some modules fail and no longer move, the shape could be reconstructed by the non-faulty modules. To do this correctly, efficiently, and without restricting the autonomy of the modules is a non-trivial task. In this paper, we study the Line Recoveryproblem, requiring the non-faulty modules to reconstruct the line without violation of connectivity requirements at any time during the recovery procedure. A thorough feasibility characterization of the problem, the necessary conditions for its solvability, and an algorithm that solves the problem, regardless of the number and distribution of faults, are provided.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".