Using the DIMMACSS-PSG Intelligent Robotic Middleware to Control Real-World and Simulated Multi-Agent Systems
Bibliographic record
Abstract
DIMMACSS-PSG is a free and open-source intelligent robotic middleware that allows a Simulink controller to control a physical real-world multi-agent system or a simulated multi-agent system existing within the Player/Stage/Gazebo general-purpose robotic simulator, using the exact same control system for both in an optimal and efficient manner. A general-purpose robotic simulator, such as Player/Stage/Gazebo, is designed to simulate everything inherent in a real-world robotics experiment, including all types of robotic hardware such as actuators and sensors (stereo-vision camera’s, moving/rotating parts, motors, etc), and provides realistic environments, physics, and sensor data. Intelligent robotic middleware, such as DIMMACSS-PSG, is the class of technologies that sit between a theoretical algorithm/function and the target real-world or simulated devices, and is required in order to actually realize an application such as one implemented on an unmanned aerial vehicle or a surface exploration robot. This paper discusses the motivation for DIMMACSS-PSG, details the important design decisions, and presents a demonstration of a Simulink controller that uses DIMMACSS-PSG to control both a simulated multi-agent system and a real-world multi-agent system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".