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Record W4389540817 · doi:10.17118/11143/21108

Real time vision-based human hand motion tracking and grasping for arobotic manipulator with soft hand

2023· article· en· W4389540817 on OpenAlexaff
Nuo Chen, Lucas Wan, Qiguang Chen, Ya‐Jun Pan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer visionRobotic handArtificial intelligenceComputer scienceTracking (education)Soft roboticsMotion (physics)Robot handMatch movingManipulator (device)Robotic armRobotPsychology

Abstract

fetched live from OpenAlex

Abstract: This paper presents a vision-based framework for the 7 degree-of-freedom (7-DOF) manipulator to imitate human hand movements for pick-and-place tasks. Using two cameras, Google’s MediaPipe is used to extract 21 key points of the human hand in each frame and generate three-dimensional (3D) coordinates of the hand. Robot Operation System (ROS) architecture is used to integrate the hardware together. The robotic system can imitate the user’s actions and achieve the goal of object pick-and-place based on the translation, rotation, and grasping of the hand. Experimental studies are carried out to demonstrate the effectiveness of the proposed work.

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: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.523

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 head0.025
GPT teacher head0.260
Teacher spread0.235 · 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
Published2023
Admission routes1
Has abstractyes

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