Friction Identification and Compensation in Robotic Manipulators
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, friction identification and friction compensation in the joints of a robotic manipulator are studied. The friction force is modeled using a single-state dynamic model that is utilized in the friction compensation algorithm. A new method for identifying the parameters of the aforementioned model in a robotic manipulator is presented. To evaluate the performance of this method, two different manipulators with different friction characteristics are examined. A 2-DOF manipulator that is used for high-speed micrometer precision manipulation and a 4-DOF macromanipulator that is used for long-reach positioning tasks are considered. It is shown that despite the different nature of the two manipulators, the same method can effectively improve the speed, accuracy, and smoothness of the manipulation in both cases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it