MétaCan
Menu
Back to cohort
Record W2586716914 · doi:10.1109/tmech.2017.2663322

A Soft-Touch Gripper for Grasping Delicate Objects

2017· article· en· W2586716914 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2017
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsSimon Fraser UniversityUniversity of the Fraser Valley
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGRASPObject (grammar)Volume (thermodynamics)Soft materialsGrippersComputer sciencePower (physics)Mechanical engineeringMaterials scienceComputer visionEngineeringArtificial intelligenceNanotechnologyPhysics

Abstract

fetched live from OpenAlex

The design of a soft-touch gripper is presented. This gripper consumes less than 60 W of power during grasp or release motions, is self-contained, inherently gentle, and can grasp delicate objects such as fruits or vegetables. The soft-touch gripper utilizes a variable-volume chamber sealed by a thin flexible latex membrane and relies on both friction between the membrane and the object being grasped, and a pressure differential between atmospheric pressure and the volume of trapped air sealed between the membrane and the object being grasped. A simplified analytical model, which can be used to estimate the grip strength of the soft-touch gripper, is developed and experimentally validated.

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 categoriesMeta-epidemiology (narrow)
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 score0.977
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.025
GPT teacher head0.264
Teacher spread0.238 · 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