A Modular Interface for Multi-Agent Marine Autonomy Simulation
Bibliographic record
Abstract
Marine autonomy is a rapidly-growing field, with wide-ranging applications in climate health monitoring, ocean exploration, and naval security. As the technologies mature, scaling is typically limited by the rate at which engineers can evaluate configuration parameters and autonomy logic in the field and in simulation. With field time already a scarce commodity, there exists a need for rapidly-repeatable tests in a reliable and flexible digital ocean twin. Such simulated tests need to be executable with a reasonable balance of fidelity, mutability, and speed. This paper presents a simulation framework that focuses on meeting these criteria within a modular, robust, and accessible software package using MIT's MOOS-IvP middleware. MOOS is a lightweight middleware designed to support interprocess communication in large, multi-application robotic platforms like AUVs (Autonomous Underwater Vehicles) and ASVs (Autonomous Surface Vehicles). Its extension, MOOS-IvP (Interval Programming), was developed around the paradigm of behavior-based autonomy, using high-level objective functions to rapidly and adaptively control speed, heading, and depth. MOOS-IvP also provides a very simple simulator and User-Interface to test these behaviors in faster-than-realtime environments. This empowers users to rapidly spin up test missions with entire fleets of autonomous agents for the purposes of fine-tuning configurations and vehicle dynamics. This paper presents a framework of thin tools along this paradigm which provides a more standard interface to the MOOS-IvP simulator and middleware. This framework, WebMOOS, aims to use modern industry standards in microservice and web architecture, along with common devops techniques like containerization to streamline the sim-to-real pipeline with science users in mind.
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.
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".