OREO: An Open-Hardware Robotic Head That Supports Practical Saccades and Accommodation
Bibliographic record
Abstract
Artificial vision algorithms are approaching human performance in many respects. However, their computational demands are also growing, which constraints their use in mobile and time-sensitive robots. Biological vision presents a well known practical solution. Specifically, primates limit the computational demands of vision by processing only small parts of the scene in detail, and frequently reorienting the eyes to different locations. Artificial vision is much more power-intensive than biological vision, so this strategy is likely to be increasingly relevant to robots, as they become more visually sophisticated. To facilitate progress and reduce duplication of work in this area, we have developed a 7-degree-of-freedom robotic head that supports such rapid saccade-like camera movements, and we are releasing the design files under an open-hardware license. The system supports C-mount cameras, large foveated lenses, and liquid lenses that allow rapid changes in focus distance. It also has a stereo baseline that is roughly the same as that of humans, to support stereo processing of nearby objects in grasping and manipulation tasks. The main contributions of this work are the mechanical design, and the demonstration that its stereo baseline, range of motion, and saccade velocity are similar to those of primate systems. The main general advantages over existing robotic heads are speed and the open-hardware license. The system would also be an ideal platform for future research that aims to replicate the performance of primate vision systems.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".