Robustness and performance trade-offs in torque control of robots with harmonic drive transmission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on designing torque control laws for robots equipped with harmonic drive transmissions. A nominal linear model of the joint is first identified from input-output experimental tests. Subsequently, by varying the input signal amplitude level, a set of models, incorporating the effect of nonlinearities in the system, can be extracted. The differences between the nominal model and this set are formulated as uncertainty bounds for control design purposes. Utilizing the uncertainty bounds, an H/sub /spl infin//-based optimal controller is designed. Experiments are performed for different uncertainty levels on the IRIS facility (a versatile, modular and reconfigurable prototype robot developed at the Robotics and Automation Laboratory of the University of Toronto).
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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