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Record W2084005769 · doi:10.1109/iros.2010.5650163

Model based deformable object manipulation using linear robust output regulation

2010· article· en· W2084005769 on OpenAlexaff
Richard Fanson, Alexandru Patriciu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)Object (grammar)Computer scienceRobust controlLinearizationController (irrigation)Robot end effectorTask (project management)PlanarArtificial intelligenceRobotComputer visionNonlinear systemControl systemControl (management)EngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents an approach to the indirect simultaneous positioning task of deformable objects based on robust linear output regulation methods. Indirect control requires maneuvering control points defined on a deformable body to desired locations by manipulating points located elsewhere on the object. The proposed control scheme uses a linearization of the deformable object dynamics into a state-space model for which the inputs are the forces applied to the manipulation points and the outputs are the positions of the control points. Then, the indirect simultaneous positioning task is treated as a classical robust linear output regulation problem. The proposed controller can compensate for deformable model nonlinearities and material uncertainties. The controller performance is illustrated through simulation and experimental results obtained on a planar deformable object model. The experiment was conducted using a robot controlled to apply desired end-effector forces on the planar object.

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.

How this classification was reachedexpand

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 score0.706
Threshold uncertainty score0.531

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 head0.059
GPT teacher head0.247
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2010
Admission routes1
Has abstractyes

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