Interpretation of mechanical impedance profiles for intelligent control of robotic meat processing
Bibliographic record
Abstract
Mechanical processing of meat and fish for packaging and marketing may involve the removal of skin, fat, and bones, and the separation of meats of different texture. In an automated processing workcell that employs robots for such purposes, proper sensing and instrumentation would be quite crucial for fast and accurate control of processing operations. In particular, force and mechanical impedance at the interface of a robotic cutter and processed object (meat) would be of significant value. Instrumentation for direct sensing would be costly and may result in a system that is noisy, less robust and sluggish. An approach has been developed where mechanical impedance is sensed by means of a software filter that uses robot/cutter motion and actuator drive current as inputs. The resulting impedance profile has to be interpreted quickly and reliably for cutter control. This paper describes the technique of impedance sensing and high-level interpretation of impedance profiles, as developed by us and implemented in a laboratory robot. Also, a hierarchical control system is described that uses impedance measurements for intelligent control of a meat processing robot. Typical results presented here have been obtained from the laboratory robot.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".