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Record W2742048498 · doi:10.1109/whc.2017.7989899

Trajectory planning and control of a belt-driven locomotion interface for flat terrain walking and stair climbing

2017· article· en· W2742048498 on OpenAlexaff
Dinh-Son Vu, József Kövecses, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsMcGill UniversityUniversité Laval
Fundersnot available
KeywordsWorkspaceComputer scienceKinematicsStairsSimulationRendering (computer graphics)Interface (matter)TrajectoryKinesthetic learningMotion planningRobotVirtual realityVirtual machineComputer visionHuman–computer interactionArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This paper presents motion planning and control algorithms for a belt-driven locomotion interface that allows the rendering of an infinite virtual environment, in which a user can walk on a level surface or walk up (or down) stairs. The paper mainly focuses on the development of the cancellation algorithm that brings the user back in the physical workspace of the locomotion interface, and thus generates the feeling of the infinite virtual environment. First, the paper summarizes the kinematics and the dynamics of the locomotion interface, the strategy implemented to lower the apparent inertia of the end-effector and to deal with the swing phase of the gait. Then, the cancellation algorithm that renders the infinite motion within the finite workspace is introduced. Finally, preliminary experiments demonstrate the use of the mechanism to render the described virtual environment, namely level floor, ascending and descending stairs.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.269
Teacher spread0.251 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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