ros4mat: A Matlab Programming Interface for Remote Operations of ROS-Based Robotic Devices in an Educational Context
Bibliographic record
Abstract
More and more, robotics is perceived in education as being an excellent way to promote higher quality learning among students, by grounding theoretical concepts into reality. In order to maximize the learning throughput, the focus of any robotics software platform should be on ease of use, with little time spent integrating the components together. To this effect, we introduce ros4mat, an open source library which provides a simple and flexible interface between ROS (Robot Operating System) and Matlab®. The conception is focused on academic use, and allows a very simple integration of sensors and actuators to existing Matlab code. The library is designed to provide an easy, platform-independent, and fast connection between a robot (running ROS) and multiple clients (running only Matlab). Moreover, it is very versatile and can be used with many common types of sensors in robotics, including low-cost ones. We report the results of ros4mat use in a robotics course to provide more real world experimentation, along with some code samples illustrating the simplicity of our approach.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.035 |
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".