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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 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.
fundA Canadian funder is recorded on the work.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.980

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 head0.016
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