Analyzing the impact of human errors on interactive service robotic scenarios via formal verification
Bibliographic record
Abstract
Abstract Developing robotic applications with human–robot interaction for the service sector raises a plethora of challenges. In these settings, human behavior is essentially unconstrained as they can stray from the plan in numerous ways, constituting a critical source of uncertainty for the outcome of the robotic mission. Application designers require accessible and reliable frameworks to address this issue at an early development stage. We present a model-driven framework for developing interactive service robotic scenarios, allowing designers to model the interactive scenario, estimate its outcome, deploy the application, and smoothly reconfigure it. This article extends the framework compared to previous works by introducing an analysis of the impact of human errors on the mission’s outcome. The core of the framework is a formal model of the agents at play—the humans and the robots—and the robotic mission under analysis, which is subject to statistical model checking to estimate the mission’s outcome. The formal model incorporates a formalization of different human erroneous behaviors’ phenotypes, whose likelihood can be tuned while configuring the scenario. Through scenarios inspired by the healthcare setting, the evaluation highlights how different configurations of erroneous behavior impact the verification results and guide the designer toward the mission design that best suits their needs.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".