Autonomous Mobile Robot That Can Read
Bibliographic record
Abstract
The ability to read would surely contribute to increased autonomy of mobile robots operating in the real world. The process seems fairly simple: the robot must be capable of acquiring an image of a message to read, extract the characters, and recognize them as symbols, characters, and words. Using an optical Character Recognition algorithm on a mobile robot however brings additional challenges: the robot has to control its position in the world and its pan-tilt-zoom camera to find textual messages to read, potentially having to compensate for its viewpoint of the message, and use the limited onboard processing capabilities to decode the message. The robot also has to deal with variations in lighting conditions. In this paper, we present our approach demonstrating that it is feasible for an autonomous mobile robot to read messages of specific colors and font in real-world conditions. We outline the constraints under which the approach works and present results obtained using a Pioneer 2 robot equipped with a Pentium 233 MHz and a Sony EVI-D30 pan-tilt-zoom camera.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".