A Multidisciplinary Approach to Learning Human-Robot Interaction (HRI) Through Real-World Problem Solving—The “BUSA Dig”
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
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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