MétaCan
Menu
Back to cohort
Record W2132923981 · doi:10.1109/iros.2011.6095078

Varying spring preloads to select grasp strategies in an adaptive hand

2011· article· en· W2132923981 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

Venue2011 IEEE/RSJ International Conference on Intelligent Robots and Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGRASPUnderactuationActuatorMechanism (biology)Spring (device)StiffnessControl theory (sociology)Computer sciencePreloadEngineeringStructural engineeringRobotArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

We describe an underactuated hand mechanism that is able to adopt a wide range of grasp types by varying the internal forces in its fingers. The adjustment is accomplished by varying the preloads of springs, which affect the grasp stability and stiffness for large and small objects. Preload adjustment can be accomplished with low power, non-backdrivable actuators in the fingers. The analysis is presented first for a planar, two-fingered hand to illustrate the trends and tradeoffs associated with variations in preload. The results are then applied numerically to a three fingered hand with three phalanges per finger. This design is a prototype for a hand to be used in an underwater oil drilling platform under conditions of low friction and uncertain object locations.

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: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.908

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 head0.146
GPT teacher head0.295
Teacher spread0.149 · 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