On Superquadric Human Modeling and Risk Assessment for Safe Planning of Human-Safe Robotic Systems
Bibliographic record
Abstract
This paper introduces a new superquadric-based human body modeling technique. The model is used as part of an on-line path planning scheme. The path planning scheme utilizes a previously proposed danger evaluation metric in which danger is characterized based on human and nonhuman factors. A new factor that accounts for the human body orientation is introduced and used along with other factors for danger evaluation. A superquadric model of the human is used to determine the values of the factors used for danger evaluation including body orientation. The resulting danger value is then used to direct the search for an alternative robot path in a direction that minimizes the danger. The use of superquadric-based human model for danger evaluation and subsequently path planning provides an accurate and computationally efficient solution. At the same time, the resulting solution guarantees a safe and danger-free path, given the factors used to characterize the danger. The approach exhibits adequate speed of decision making, rendering it potentially suitable for real-time applications involving human-robot interaction. The proposed method is evaluated using a CRS-F3 industrial manipulator through various case studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".