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Record W2110358632 · doi:10.1109/robot.2001.932949

Kinematic feasibility analysis of 3D grasps

2002· article· en· W2110358632 on OpenAlex
Yisheng Guan, H. Zhang

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
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGRASPKinematicsObject (grammar)Nonlinear systemSet (abstract data type)Computer scienceMathematical optimizationOptimization problemNonlinear programmingMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

We present a solution to the problem of determining if it is possible for a given dexterous hand to grasp a polyhedral object at a desired topological configuration, subject to kinematic constraints. We refer to this problem as kinematic feasibility analysis. In our study, we define a desired grasp in terms of a set of contact pairs between the topological features of the hand and the object, and formulate a general algorithm that makes use of constrained optimization with nonlinear constraints to determine the feasibility. We first derive the conditions in order for the hand to make the desired contact and avoid undesirable collision. These conditions are expressed in terms of equalities and inequalities in the configuration variables of the hand and object, which can then easily be transformed into a constrained nonlinear optimization problem. Numerical examples are provided to illustrate the solution.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.052
GPT teacher head0.248
Teacher spread0.196 · 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

Quick stats

Citations7
Published2002
Admission routes1
Has abstractyes

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