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Record W2554204513 · doi:10.1115/1.4035171

A Haptic Bilateral System for the Remote Human–Human Handshake

2016· article· en· W2554204513 on OpenAlexaff
Nicolò Pedemonte, Thierry Laliberté, Clément Gosselin

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2016
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHandshakeHaptic technologyComputer scienceInterface (matter)Transparency (behavior)Human–computer interactionSoftwareHuman interface deviceTeleoperationSimulationRobotArtificial intelligenceAsynchronous communicationTelecommunicationsOperating systemComputer security

Abstract

fetched live from OpenAlex

The objective of this work is to develop a hardware and software system that allows two people to shake hands while being in different locations. To this end, a novel haptic interface that is capable of performing a robotic handshake was designed and built. In this paper, the communication system composed of two such interfaces is presented. The working principle of the haptic interface is first recalled. The attention is then focused on the development and the implementation of a control algorithm that allows bilateral interaction between the two users. Finally, the transparency of the system is qualitatively validated in terms of the exchanged forces and of the positions of the interfaces, through some preliminary experiments.

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)0.001
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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.224
Teacher spread0.201 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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