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Record W2107323031 · doi:10.1109/tra.2003.810235

Kinematic feasibility analysis of 3-D multifingered grasps

2003· article· en· W2107323031 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

VenueIEEE Transactions on Robotics and Automation · 2003
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGRASPKinematicsObject (grammar)Computer scienceProcess (computing)Set (abstract data type)Nonlinear systemMathematical optimizationSoftwareArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Planning of a dextrous manipulation task for a multifingered hand requires the feasibility of all the grasps involved throughout the manipulation process. In this paper, we address the problem of determining whether a desired grasp of a polyhedral object is kinematically feasible. In our study, we define a grasp in terms of a system of contact pairs between the topological features of the hand and the object, and formulate the grasp feasibility analysis as a set of equality and inequality constraints in the variables of the hand and object configurations. The feasibility of a grasp then becomes equivalent to the simultaneous satisfaction of all the constraints. This allows us to cast the feasibility analysis conveniently as a constrained nonlinear optimization problem and solve it numerically with commercially available software. The effectiveness of our approach is illustrated with an example of grasping a cuboid using a three-fingered robotic hand.

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 score0.885
Threshold uncertainty score0.382

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)0.0000.000

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 head0.027
GPT teacher head0.250
Teacher spread0.223 · 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