An Offline-Merge-Online Robot Teaching Method Based on Natural Human-Robot Interaction and Visual-Aid Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This article proposes an offline-merge-online robot teaching method (OMORTM). Specifically, a virtual-real fusion interactive interface (VRFII) is first developed by projecting a virtual robot into the real scene with an augmented-reality (AR) device, aiming to implement offline teaching. Second, a visual-aid algorithm (VAA) is proposed to improve offline teaching accuracy. Third, a gesture and speech teaching fusion algorithm (GSTA) with the fingertip tactile force feedback is developed to obtain the natural teaching pattern and improve the interactive accuracy of teaching the real or virtual robot. More specifically, through the VRFII, the operator can use the GSTA and the VAA to teach the virtual robot naturally and safely, and then the real robot reproduces the motion of the virtual robot. Therefore, OMORTM enables the teaching results to be quickly verified while ensuring the operator's safety and avoiding damage to the robot or workpiece. A series of experiments were conducted to validate the practicality and effectiveness of OMORTM. The results show that by effectively combining the offline and online, OMORTMprovides accurate robotic teaching processes, suitable for nonprofessionals.
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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 | 0.002 |
| 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