Leveraging Mixed Reality Infrastructure for Robotics and Applied AI Instruction
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
Mixed reality is an important classroom tool for managing complexity from both the students' and instructor's standpoints. It can be used to provide important scaffolds when introducing robotics, by allowing elements of perception and control to be abstracted, and these abstractions removed as a course progresses (or left in place to introduce robotics to younger groups of students). In prior work, we have illustrated the potential of this approach both in providing scaffolding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this paper, we explore integrating extensions and improvements to the mixed reality components themselves as part of a course in applied artificial intelligence and robotics. We present a set of assignments that in addition to exploring robotics concepts, actively integrate creating or improving mixed reality components. We find that this approach better leverages the advantages brought about by mixed reality in terms of student motivation, and also provides some very useful software engineering experience to the students.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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