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Record W2152912123 · doi:10.1109/ccece.2008.4564681

A survey on real-time controlled multi-fingered robotic hand

2008· article· en· W2152912123 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGRASPComputer scienceRobotic handObject (grammar)Artificial intelligenceRobotGrippersComputer visionArchitectureRange (aeronautics)Robot handMode (computer interface)Human–computer interactionSimulationEngineeringSoftware engineering

Abstract

fetched live from OpenAlex

Based on a wide range of literature review, the basic concepts and methodologies of real-time controlled robotic hand grasping system are reorganized, presented and illustrated in this paper. Two different taxonomies of the grasp mode are described here based on the connectivity between the robot hand and the object, and the geometry of the object respectively. A generic architecture for vision guided grasping system is illustrated which could be used as a reference for designing any other grasping system in related fields. The criteria used for evaluating generated grasps are listed and a brief introduction of a useful 3D simulator ldquoGraspIt!rdquo is given in the corresponding section. In the last part of the paper, three types of approaches that drive the grasping system and some of their counter applications are presented.

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: Empirical
Teacher disagreement score0.169
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.206
Teacher spread0.173 · 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