Safe Human-Robot-Collaboration (HRC) based on a new concept considering human movement variability: Presentation held at IROS 2017, Friendly People, Friendly Robots, Vancouver, September 24 - 28, 2017
Bibliographic record
Abstract
3D-simulation of human-robot work places and processes allows the prediction of failures during manufacturing at an early stage. Furthermore, it helps to optimize the production processes. In human robot collaboration (HRC) scenarios, while machines and robots work reliably and predictably, humans can vary in their movements and actions without an obvious reason. This is a particular challenge during the planning and simulating of HRC processes. For safe and realistic simulation of Human-Robot-Collaboration-scenarios, existing digital human models should be further developed taking into account the variability of human movements. This paper will represent a new concept considering the human movement variability in HRC applications. This concept will be later integrated in 3D-simulation for insuring the safety and efficiency of HRC-processes during the offline planning phase. Furthermore, the standard safety requirements and industrial standards in HRC applications will be also considered in this approach. As a result, this paper shows a procedural method in which the human movement variability in HRC can be examined on the basis of empirical studies. The results of these studies will be used to describe the human movement variability in HRC in a mathematical model. In further developments this model will be implemented for the automatic design of the safety technology in HRC applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".