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Record W1971241999 · doi:10.1115/detc2012-70705

On the Design of Mechanically Programmable Underactuated Anthropomorphic Robotic and Prosthetic Grippers

2012· article· en· W1971241999 on OpenAlexaff
Mathieu Baril, Thierry Laliberté, Clément Gosselin, François Routhier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsUnderactuationGrippersMechanism (biology)ThumbComputer scienceSynchronization (alternating current)EngineeringControl engineeringRobotArtificial intelligenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents several innovative features that aim at improving the mechanical design of underactuated anthropomorphic grippers. Based on prototypes previously developed, the characteristics of a tendon-driven underactuated finger and a reconfigurable thumb are first presented. Their geometry, coverings and attachment principles are detailed. Then, a novel approach to mechanically couple the thumb with the four fingers is presented. Using a lever, this approach provides the ability to mechanically prescribe a desired distribution of the forces/velocities during the actuation. A static model is developed to visualize the possibilities offered by this principle. Also, a compact mechanism that allows underactuation between the four fingers is described. This mechanism significantly improves the synchronization of the outputs. Additionally, a mechanical selector is introduced that makes the gripper mechanically programmable by allowing to selectively block one or many outputs. The use of this mechanical selector, combined with the reconfigurable thumb and the underactuation between the fingers, allows a gripper to produce several grasping modes without the need of additional actuation. Finally, a prototype including all these features is briefly described.

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: none
Teacher disagreement score0.936
Threshold uncertainty score0.462

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 head0.042
GPT teacher head0.229
Teacher spread0.187 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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