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Record W2618876269 · doi:10.1109/syscon.2017.7934753

Fuzzy controlled object manipulation using a three-fingered robotic hand

2017· article· en· W2618876269 on OpenAlexaff
Vinicius Prado da Fonseca, Daniel John Kucherhan, Thiago Eustaquio Alves de Oliveira, Da Zhi, Emil M. Petriu

Bibliographic record

Venue2017 Annual IEEE International Systems Conference (SysCon) · 2017
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUnderactuationGRASPComputer scienceRobotic handArtificial intelligenceObject (grammar)ThumbRoboticsOrientation (vector space)TrajectoryRobot end effectorComputer visionDegrees of freedom (physics and chemistry)Control engineeringGrippersRobotControl theory (sociology)EngineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

Use of underactuated fingers to conduct precision, in-hand manipulation is a common topic of recent robotics research, mostly due to their relatively light weight and simplicity of use. Grasping operations are facilitated by compliant joints however precise, in-hand manipulation is more challenging since post-grasp orientation of an object varies. Underactuated, robotic-fingered hands that are capable of predictable grasping are one step closer to human-like end-effectors. This paper presents a new effort towards effective robotic manipulation using two underactuated fingers and one fully actuated robotic thumb with 3 degrees of freedom (DOF). Fuzzy grasping using tactile feedback is used to provide an enhanced stable grasp solution. The system comprises tactile feedback, orientation of underactuated phalanges using flexible joints, and thumb trajectory planning.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.093
GPT teacher head0.311
Teacher spread0.218 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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