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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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.386

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 head0.026
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 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

Citations1
Published2002
Admission routes1
Has abstractyes

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