Practical Considerations of Uncalibrated Visual Servoing
Bibliographic record
Abstract
Visual Servoing (VS) has been researched for over forty years, but real-world adoption has been slow. Challenges include camera calibration, lack of, or difficulty, integrating reliable real-time visual trackers and a lack of simple control interfaces through which robots can be controlled. Uncalibrated Visual Servoing (UVS) presents a viable approach to facilitate robot control and task definition in unstructured environments. Tasks are defined through visual features directly in image space. By estimating the full non-parametric image Jacobian no a-priori models or camera calibration is required. In practice UVS is highly dependant on camera positioning, visual tracker performance, and the underlying robot control. In this paper we explore theperformance of UVS with respect to these dependencies, both, in simulation, and with a physical robot. Through the use of ROS-UVS, our open source Uncalibrated Visual Servoing library, we hope that characterizing the behaviour of UVS will help facilitate adoption for new users and serve to showcase the features and practical applications of our library.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".