A symbolic approach to determining exciting trajectories for identification of manipulator dynamic models
Bibliographic record
Abstract
In experiments aimed at the identification of parameters in the dynamic model of a robotic manipulator, the use of a trajectory that excites the parameters so that the numerical estimation procedure is well-conditioned is important for an accurate estimation. This paper presents a new approach, based upon symbolic computations, to determine such "exciting trajectories". Herein, the concept of mutually productwise odd (MPO) functions is introduced, and the problem of determining an exciting trajectory is reformulated as one of rendering time histories of the regressor functions of the associated dynamic equation MPO. Parametrized joint-angle time histories whose parameters take on a discrete set of values form the search space of an algorithm that renders the regressor time histories MPO. The overall approach exploits the use of test motions to reduce the complexity of the search procedure. The approach is successfully applied to a three-link spatial manipulator, thereby showing how all required inertial parameters can be identified using exciting trajectories.>
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".