An adaptive control strategy for robotic cutting
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
Automation of a cutting process requires a system that is able to perform a planned cutting work irrespective of the interaction forces experienced during this process. For a robotic cutter the controller must adjust the applied force exerted on the material to be cut, while complying with unknown environment restrictions and disturbances. In this paper the problem of cutting a media by a blade is investigated. Based on a learning philosophy and position and velocity errors feedback an algorithm for the control of such a process is presented. An adaptive position controller and a switching logical velocity controller ensure the cutting operation is performed along a desired path with a desired velocity, while a learning control strategy is employed to adjust the required cutting force. The latter is a force learning algorithm which uses the force feedback. The effectiveness of the presented control system is demonstrated by simulation results.
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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) | 0.001 | 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 it