An Application of Campro-R (Mobile Robot with Camera and Projector) at home - A speculation about structuring information indoors
Bibliographic record
Abstract
We are developing a new robot service using a mobile robot with a camera and a projector in anticipation of the coming symbiosis of society with robots. We constructed a (Campro-R) system by placing a camera and projector on a mobile robot. Campro-R supports a wide variety of information-projecting-services. A typical information display service is to project an image on a real object so the information is just like a real sticky note. One problem is that the robot suffers from significant dead reckoning errors which yields errors in projecting the information, i.e. there are errors in "the environment model for projection of information". Therefore, we propose here a new concept that reduces the errors in developing the environmental model for ubiquitous information display by allocating markers in the real world according to need. We discuss the trade off between the number of the visual markers and the projection error. Finally, we show the results of a preliminary experiment conducted for assessing the impact of misalignment of projected information
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".