Experimental results for output feedback adaptive robot control
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines three methods of adaptive output feedback control for robotic manipulators. Implementing output feedback control allows use of only the position information, which can be measured quite accurately. Velocity and acceleration measurements can get corrupted by noise. A method proposed by K. W. Lee and H. K. Khalil [Adaptive output feedback control of robot manipulators using high-gain observer, Int. J. Control , 6 , 869–886 (1997)] using a high-gain observer, one proposed by J. J. Craig, P. Hsu and S. S. Sastry [Adaptive control of mechanical manipulators, Int. J. Robot. Res. , 6 (2), 16–27 (1987)] with the addition of a linear observer that we propose, and a method proposed by R. Gourdeau and H. M. Schwartz [Adaptive control of robotic manipulators: Experimental results, Proceedings of the 1991 IEEE International Conference on Robotics and Automation (Apr. 1991) pp. 8–15] using an Extended Kalman Filter are examined. The methods are implemented in simulation and experimentally on a direct-drive robot. The performance of each of the algorithms is compared.
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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