AN AGENT-BASED APPROACH TO INTRODUCTORY ROBOTICS USING ROBOTIC SOCCER
Why this work is in the frame
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Bibliographic record
Abstract
Introducing robotics to undergraduates is a challenging problem from an educational standpoint. The overlap to numerous areas of applied computer science, and the number of difficult subproblems that must be dealt with in any nontrivial robotics application (e.g., vision, path planning, real-time computing), easily overwhelm students at the undergraduate level. We have been employing robotic soccer as a vehicle to motivate undergraduate students in robotics, in order to provide students with an interesting domain that embodies significant research challenges in artificial intelligence. In order to manage the complexity of the domain, we employ software agents in several key components in our approach, and as a methodology for student implementations. This paper describes our strategies for introducing the elements of soccer playing to undergraduate robotics students, and the agent-based approach we employ.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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 it