MétaCan
Menu
Back to cohort
Record W1641694943 · doi:10.1109/robot.1995.525469

Definition and force distribution of power grasps

2002· article· en· W1641694943 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
TopicRobot Manipulation and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGRASPLinear subspaceObject (grammar)Power (physics)Robot handComputer scienceArtificial intelligenceTactile sensorComputer visionContact forceRobotGrippersSpace (punctuation)Robotic handMathematicsEngineeringGeometryPhysicsClassical mechanicsMechanical engineering

Abstract

fetched live from OpenAlex

This research treats grasping of an object by a multifingered robot hand. By decomposing the space of contact forces exerted between the fingers and the grasped object into subspaces we develop a method to determine the dimensions of the subspaces with respect to the connectivity of the grasped object. This approach provides insight into different grasps based on a classification into three types. A power grasp is defined when the connectivity of the grasped object is equal to or less than zero. The analysis of contact force distribution is simplified for a power grasp with zero connectivity. Examples for dimensional determination are illustrated.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.391

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 head0.025
GPT teacher head0.189
Teacher spread0.164 · 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

Citations42
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicRobot Manipulation and LearningFrench-language works237,207