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Record W3148946639 · doi:10.1109/lra.2021.3068976

On the Optimal Design of Underactuated Fingers Using Rolling Contact Joints

2021· article· en· W3148946639 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

VenueIEEE Robotics and Automation Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsUnderactuationRevolute jointJoint (building)Optimal designEngineeringComputer scienceRobotStructural engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The design of underactuated fingers using rolling contact joints is addressed in this article in order to increase performance in robotic and prosthetic hands applications. Rolling contact joints have proven to be a promising alternative to revolute joints by increasing the range of motion, introducing compliance and decreasing internal friction. The conditions of optimal design for fingers using such joints are however not well stated. In order to fully exploit the potential of rolling-contact-joint based fingers, their design space is extended and performance metrics adapted to underactuated fingers are used in this letter. An optimization based on these metrics is performed over the large design space using genetic algorithms to find an optimal design. The solution obtained is then used to build a prototype and its performance is compared to that predicted by the model and to a comparative design using a realistic test bench.

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: none
Teacher disagreement score0.633
Threshold uncertainty score0.317

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 head0.045
GPT teacher head0.239
Teacher spread0.194 · 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