A Multidisciplinary Approach to Learning Human-Robot Interaction (HRI) Through Real-World Problem Solving—The “BUSA Dig”
Bibliographic record
Abstract
This article examines a cross-disciplinary approach to learning human-robot interaction (HRI) through real-world problem solving. The problem originated from the need of archaeologists at the University of California, Berkeley, and Ryerson University to safely explore archaeologically significant areas disturbed by heavy looting activities at the ancient site of el-Hibeh, Egypt. The learning objectives were developed through interdisciplinary collaboration of three departments at Ryerson University. The deliverable was an HRI final examination---known as the BUSA Dig---in which students teleoperated a robot of their own design and manufacture that explored and mapped a simulated archaeological site. The students participated in the examination through their membership in one of six mixed groups composed of undergraduate computer science and graduate digital media students. At the end of the exam, students were expected to understand and explain HRI principles, paradigms, and metrics, construct appropriate robots that could survive and function in a defined environment, and employ mobile and teleoperated robots that solved problems.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".