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Record W2109998105 · doi:10.1109/achi.2009.31

Deformation Planning for Robotic Soft Tissue Manipulation

2009· article· en· W2109998105 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsJacobian matrix and determinantComputer scienceEllipsoidKernel (algebra)Deformation (meteorology)Boundary (topology)Point (geometry)Computer visionArtificial intelligenceObject (grammar)Position (finance)MathematicsGeometryPhysicsMathematical analysisApplied mathematics

Abstract

fetched live from OpenAlex

This paper presents a model based approach to the soft tissue deformation planning. The deformable object is manipulated through boundary displacements induced by robot manipulators controlled in position. The manipulated boundaries are maneuvered such that the control points defined on the deformable object converge to the desired locations. The proposed control is based on a Jacobian transformation between the set of manipulated point displacements and the control point displacements computed using a meshless model (reproducing kernel particle method - RKPM) of the deformable object. RKPM is employed for this study as it has been proven to accurately handle large deformations and requires no re-meshing algorithms. Simulations show that a model with a coarse particle grid can produce Jacobian transforms that accurately control a more physically real and refined model. The next step is to perform a physical study on a tissue phantom interacting with a dual arm manipulator.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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