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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 OpenAlex
Vinicius Prado da Fonseca, Daniel John Kucherhan, Thiago Eustaquio Alves de Oliveira, Da Zhi, Emil M. Petriu

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

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.

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), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
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 head0.093
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