An accurate and computationally efficient method for whole-body human modeling with applications in HRI
Bibliographic record
Abstract
Interactive robots are required to have minimal footprint on the shop floor and to be able to work in constrained areas while ensuring the safety of the humans. To this end, modeling of an unstructured environment including the humans is an indispensable part of the online control schemes deployed in such robots. In this regard, a new approach is proposed for generating an efficient model of the human body. This model takes advantage of superquadric functions to represent the human body more realistically than using primitive shapes, while offering minimal computational complexity and simplicity of further computations in comparison to the existing articulated models. This approach is also capable of incorporating various body postures and arbitrary arm configurations in the model. In addition, a new and intelligent sensory system, called floor mat, is introduced which can significantly contribute to generation of the proposed model, in a timely manner. The integration of the floor mat in a multi-sensory system for obtaining all required data for rendering the real-time 3D human model is then discussed. The use of superquadric-based human model in conjunction with the proposed sensing techniques provides an accurate, yet computationally efficient solution for safe human-robot interactions (HRI).
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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".