Superquadric obstacle modeling and a danger evaluation method with applications in safe planning for human-safe industrial robots
Bibliographic record
Abstract
This paper presents a human body modeling technique, and a safe planning strategy using danger evaluation for robotic manipulators intended for interactions with humans. Such interactive robots are required to have minimum footprint on the shop floor and to be able to work in constrained areas while ensuring the safety of the humans. A new approach is proposed for generating an efficient representation of the human body by considering its dimensions, position, and orientation. This model takes advantage of superquadric functions to represent the human body more realistically than using primitive shapes. By taking advantage of this model, a new measure of danger involved in robot operation is proposed. This approach significantly improves the effectiveness of danger evaluation by considering a more precise body model. The danger index is then used as part of the path planning algorithm to guarantee the safety of operations. The method is evaluated on a CRS-F3 industrial manipulator.
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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".