MétaCan
Menu
Back to cohort
Record W2977177224 · doi:10.1109/iccse.2019.8845064

Development and Analysis of a Hybrid Inverse Kinematic Solution for Anthropomorphic In-Hand Manipulations

2019· article· en· W2977177224 on OpenAlexaff
Matthew Levins, Haoxiang Lang, Ming Li, Ying Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFlexibility (engineering)Computer scienceRobotic handKinematicsArtificial intelligenceVariety (cybernetics)RobotHuman–computer interactionInverse kinematicsControl engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

The evolutionary success of humans is a result of many biological factors; however, one predominant advantage is from the capabilities of the human hand. The dexterity of this mechanical system provides the capabilities to perform a wide variety of tasks. This performance is the motivation to design robotic systems with similar characteristics. Anthropomorphic robotic hands are designed to be the robotic counterpart of human hands. These human-like systems attempt to achieve the same dexterity as human hands to allow flexibility in the applications that require robotic assistance. Controlling these complex systems serves as a challenge in the robotic community and currently limits the capabilities of these advanced robot hands. This paper proposes a solution for controlling objects during in-hand manipulations with inspiration from parallel manipulators. This solution shows promising results that offer the basis for further research into more complex tasks.

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 score0.231
Threshold uncertainty score0.224

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 head0.031
GPT teacher head0.248
Teacher spread0.217 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicRobot Manipulation and LearningFrench-language works237,207