A modular object-oriented framework for hierarchical multi-resolution robot simulation
Bibliographic record
Abstract
We describe the design and implementation of RSTATION, an object-oriented, modular robot simulator with hierarchical analysis capabilities. Modularity is achieved via the features of design encapsulation and enables grouping a set of interconnected components into a single component and dividing the robot system into several sets of subordinate modules recursively. By careful construction of the data types and classes, RSTATION allows for hierarchical simulation of the kinematics, and the dynamics at three levels: considering only main links (high-level), using simplified models including dynamic properties of transmission elements (intermediate level), and taking into account the detailed kinematics and dynamics of transmission elements (low-level). Submodules can be set to different resolution during a single simulation. The data types and classes also exploit a recent set of coordinate invariant robot analysis algorithms based on modern screw theory. Central to the low-level dynamic analysis capability is an algorithm for systematically extracting the constraint equations for general gearing systems. The various features of RSTATION are illustrated with a detailed case study of a commercial industrial robot.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".