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Record W1992937093 · doi:10.1109/mra.2013.2283651

A Friendly Beast of Burden: A Human-Assistive Robot for Handling Large Payloads

2013· article· en· W1992937093 on OpenAlexafffund
Clément Gosselin, Thierry Laliberté, Boris Mayer-St-Onge, Simon Foucault, Alexandre Campeau‐Lecours, Vincent Duchaine, Noemie Paradis, Dalong Gao, Roland Menassa

Bibliographic record

VenueIEEE Robotics & Automation Magazine · 2013
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of Canada
KeywordsRobotActuatorTorqueEngineeringInterface (matter)Control engineeringRobot end effectorBackupTransmission (telecommunications)SimulationComputer scienceElectrical engineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

This article presents a novel robotic assistive device for the handling of large payloads. The design of the robot is based on the application of the following fundamental mechanical principles: inertia is minimized, a parallel closed-loop cable/belt routing system is used to kinematically decouple the transmission from fixed actuators and to the end-effector, and variable static balancing is used to minimize the actuation forces required for vertical motion. As a result, the device requires only low power, thereby improving safety, and can be operated manually, even in the event of a power failure (with minimum backup power for brake release). A novel force/torque sensor is also introduced along with a control algorithm based on variable admittance that provides a very intuitive interface for physical human-robot cooperation. Finally, a full-scale prototype integrating all of the above concepts is 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.

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)0.001
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.256
Teacher spread0.241 · 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

Citations59
Published2013
Admission routes2
Has abstractyes

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