A Systems Engineering Approach to High-Level Task Execution: A Case Study in Robotic Lawn Mowing Using LIMO and Gazebo
Why this work is in the frame
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Bibliographic record
Abstract
The successful deployment of autonomous systems hinges on the effective integration of perception, planning, and control subsystems. This paper presents a systems engineering case study focused on the verification and validation of a high-level task scheduling framework in the context of service robotics. We demonstrate the feasibility of this framework by applying it to a structured lawn mowing scenario, where the high-level execution plan is generated by the framework and translated into actionable commands within a Gazebo simulation. Using a LIMO robot model, we implement the complete plan in a realistic simulation environment, validating both the interoperability of system components and the practicality of the abstract plan. The results confirm that the framework's output can be effectively interpreted and executed on a realworld robot model, demonstrating a critical step in the systems engineering life cycle. This work provides a concrete methodology for validating abstract planning frameworks through simulation and reinforces the value of integrated, simulationbased verification in robotics.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it