The Use of Harr-like Features in Bubblegrams: a Mixed Reality Human-Robot Interaction Technique
Bibliographic record
Abstract
We present the application of a vision algorithm based on Harr-like features in Bubblegrams - a new mixed reality-based human-robot interaction (HRI) technique. Bubblegrams allows humans and robots working on collocated synchronous tasks to interact directly by visually augmenting their shared physical environment. Bubblegrams uses comics-like interactive graphic balloons or bubbles that appear above the robot s body and allow intuitive interaction with the robot. Users wear light-weight mixed reality goggles that integrate displays and a camera, allowing the user to view and interact with the physical environment as well as with the virtual Bubblegrams interface linked to the robot s body. In order to efficiently link Bubblegrams in real-time to the physical robot we implemented a vision algorithm based on Harr-like features which is the main topic of this paper. This paper briefly details the design of the Bubblegrams interface, the hardware and software we use for the current prototype, and the full details of the vision algorithm.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".