Board 64: ROS as an Undergraduate Project-based Learning Enabler
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".