MétaCan
Menu
Back to cohort
Record W1987010947 · doi:10.1109/robot.2007.363900

On with the Visuomotor Function: A 6DOF Adaptive Approach for Modeling Image-Based Variations and Visual Servoing

2007· article· en· W1987010947 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings - IEEE International Conference on Robotics and Automation/Proceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVisual servoingArtificial intelligenceComputer visionImage (mathematics)Computer scienceFunction (biology)Transformation (genetics)Euclidean spaceRobotMathematics

Abstract

fetched live from OpenAlex

In this paper, we proposes a visual servoing method that approximates the relation between the variations of image points and the variations of a stereo rig in Euclidian space. As with most image-based visual servoing methods, commands are expressed in the space of image features. However, instead of relating instantaneous image-based variations to instantaneous variations in Euclidian space, the visuomotor function relates arbitrary image-based variations to Euclidian transformations. The visuomotor function is approximated in real-time by using online estimation techniques. The system improves its performance with experience and is able to adapt to different configurations of the cameras or environment. Given the disparities between two sets of corresponding image points, the visuomotor function provides the Euclidian transformation the robot must execute in order to align the image coordinates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.297
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it