Board 64: ROS as an Undergraduate Project-based Learning Enabler
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
Abstract Future engineering and science jobs will require a greater degree of speciality and diversity at the same time. In manufacturing and service industries robots will likely play a huge job generator. Self driving cars, trucks, and humanoids will only be the start. Advanced robots have traditionally been taught heavily at the graduate level, but not until recently at the undergraduate level. However, the Robotic Operating System (ROS) is a game changer in this regard. ROS allows programmers and engineers to tackle extremely difficult problems without specific knowledge of some of the components. In this paper we look at a year long study of robotic arm mechanisms using a PBL technique. We detail the learning difficulties encountered when developing a program from scratch as well as some of the successes. As part of our measurement of merit, we provide our materials on the internet and track their usage by others. Details of where and how we obtained our data are also provided. The current project is based on the Kobuki Turtlebot and the Trossen Robotics Arm Pincher. In this PBL we attempt to mount a robotic arm on the Turtlebot to retrieve objects located in remote locations using a previously built map. Then building off other student projects we attempt to extend our Kobuki's capabilities from basic navigation to navigation with a mission and purpose.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.001 |
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