MétaCan
Menu
Back to cohort
Record W2153908391 · doi:10.5194/ms-1-13-2010

A proposal for benchmark tests for underactuated or compliant hands

2010· article· en· W2153908391 on OpenAlexaboutno aff
Gert A. Kragten, C. Meijneke, Just L. Herder

Bibliographic record

VenueMechanical sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsUnderactuationGRASPBenchmark (surveying)Object (grammar)Computer scienceMeasure (data warehouse)Artificial intelligenceRobotData mining

Abstract

fetched live from OpenAlex

Abstract. There is a lack of agreement in the literature as to what exactly quantifies the performance of underactuated hands. This paper proposes two benchmark tests to measure the ability of underactuated hands to grasp different objects and the ability to hold the objects when force disturbances apply. The first test determines the smallest and largest cylindrical object which can be successfully grasped in an enveloping grasp or in a pinch grasp. The second test provides the maximal allowable force which can be applied to a grasped object without loosing it. A setup was constructed consisting of standard components. Exemplary tests were applied to the Delft Hand 2. The proposed benchmark tests are representative to quantify the performance of pick and place operations with underactuated hands. The results of the tests can be applied to evaluate, compare, and improve the performance of robotic hands. This paper was presented at the IFToMM/ASME International Workshop on Underactuated Grasping (UG2010), 19 August 2010, Montréal, Canada.

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)0.009
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.074
GPT teacher head0.333
Teacher spread0.258 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations23
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMechanical sciencesSame topicRobot Manipulation and LearningFrench-language works237,207