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Record W1760091227 · doi:10.1109/isic.1993.397711

Unified control of contact and joint space drift in multifingered robot hands

2002· article· en· W1760091227 on OpenAlexaff
A. Kapitanovsky, James K. Mills, A.A. Goldenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRedundancy (engineering)KinematicsRobotControl theory (sociology)Nonholonomic systemComputer scienceRobot kinematicsContext (archaeology)TrajectoryArtificial intelligenceComputer visionControl (management)Mobile robotPhysics

Abstract

fetched live from OpenAlex

A general approach for controlling the system configuration drift resulting from the presence of nonholonomic constraints and kinematic redundancy in multifingered robot hands is presented. A redundant multifingered robot hand for grasping and manipulating an object using 3-D rolling contacts is considered. The configuration space of a multifingered robot system (i.e., the hand and a grasped object in contact) is described in terms of finger joint variables and contact variables. In the context of motion repeatability, the combined effect of the redundancy in the system generalized variables and the non-integrability of the hand kinematics equations generate undesirable drift of both finger joint variables and finger/object contact locations. A stable method of drive compensation is proposed for controlling a general case of redundant 3-D rolling robot hands that manipulate an object along a given trajectory.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.198
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2002
Admission routes1
Has abstractyes

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