Bibliographic record
Abstract
A heavy payload attached to the wrist force/moment (F/M) sensor of a manipulator can cause the conventional impedance controller to fail in establishing the desired impedance due to the noncontact components of the force measurement, i.e., the inertial and gravitational forces of the payload. This paper proposes an impedance control scheme for such a manipulator to accurately shape its force-response without needing any acceleration measurement. Therefore, no wrist accelerometer or a dynamic estimator for compensating the inertial load forces is required. The impedance controller is further developed using an inner/outer loop feedback approach that not only overcomes the robot dynamics uncertainty, but also allows the specification of the target impedance model in a general form, e.g., a nonlinear model. The stability and convergence of the impedance controller are analytically investigated, and the results show that the control input remains bounded provided that the desired inertia is selected to be different from the payload inertia. Experimental results demonstrate that the proposed impedance controller is able to accurately shape the impedance of a manipulator carrying a relatively heavy load according to the desired impedance model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".