Automated extraction and checking of property models from source code for robot swarms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As robots become a common presence in our everyday lives, ensuring the security and safety of robotic systems becomes an increasingly important and urgent challenge. Multi-robot systems, in particular, have the potential to revolutionize multiple industries---such as transportation and home care---where safety guarantees are a primary requirement. A known challenge for swarms and multi-robot systems is the gap between requirements and design, due to the need to translate swarm-level objectives into robot-level behaviors. In this paper, we focus on a less studied problem---the gap between requirements and implementation. As a case study, we use Buzz, that is a dynamic programming language designed for swarm robotics applications. Similarly to Python, Lua, and JavaScript, Buzz does not natively offer formal guarantees of correctness or safety. We propose an approach to automatically extract "as-implemented" models from Buzz programs, whose properties can then be formally analyzed and verified. Results obtained from the experiments performed on two medium-size open-source production-level systems for robotics research have also been reported. Our results show that the approach is feasible and is scalable to larger systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it