Automatic generation of nonlinear task-based transformations for robot contact control implementation
Bibliographic record
Abstract
Many sophisticated robot controls are formulated with nonlinear Jacobian transformations, which are dependent on task geometry, as integral components of the control law. Under laboratory conditions, these controls are typically implemented with these nonlinear, task geometry dependent transformations analytically derived and hard-coded in controller software. A change in task geometry implies a change in controller software, a cumbersome exercise that severely restricts the versatility of such controls. In this brief paper, we propose a simple, yet effective algorithm to circumvent this implementation problem. Using only geometric information of the task, the required nonlinear task geometry dependent Jacobian transformations are approximated numerically. While seemingly trivial, this methodology implies that given new task geometry data, these sophisticated task based controls can be utilized without tedious derivation and coding of controller software, a process that effectively renders such controls of little utility in an industrial setting. To illustrate the algorithm proposed, two examples are given with experimental results of one example to contrast the performance of the proposed algorithm with that using hard-coded transformations.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".