Uncalibrated visual servoing using a biased Newton method for on-line singularity detection and avoidance
Bibliographic record
Abstract
While in calibrated settings trajectories can be planned so to avoid singular or poorly observable configurations, in uncalibrated visual servoing in general a priori information about singularities (visual or robotic) may be unavailable. Instead we propose a method where trajectories are corrected online to avoid singular and near singular regions. Mathematically this is achieved using a so called nullspace-biased Newton step in a visual servoing with a Broyden type Jacobian estimation. The bias is applied so to first hand use (any) robot redundancy and thus not compromise the visually specified aspects of the trajectory. The closeness to a singular region is measured online from the estimated visual motor Jacobian. We also illustrate how to apply the bias method for simple visual obstacle avoidance. To show the practical applicability of our method we have applied it to Barrett WAM and PUMA560 manipulators and tested both numerous real trajectories, as well as run exhaustive simulations around critical configurations using a simulation model to confirm empirically that both safe and efficient trajectories are chosen around singular regions.
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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 | 0.000 | 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".