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Record W2158799378 · doi:10.1177/0278364905055419

Foot Placement Selection Using Non-geometric Visual Properties

2005· article· en· W2158799378 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.

Bibliographic record

VenueThe International Journal of Robotics Research · 2005
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Waterloo
FundersOffice of Naval Research
KeywordsFoot (prosody)Computer scienceGaitPosition (finance)Selection (genetic algorithm)Artificial intelligencePath (computing)Mechanism (biology)Key (lock)Computer visionSimulationPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Geometric information alone is not sufficient to guide foot placement in arobotic device. A travel path may be firm or soft, slippery or sticky, and will thus affect locomotion. In humans, associations are made between various travel surfaces and gait modification. If a preferred foot position is unavailable, an alternate is selected. This selection is biased, and not random. Here we present a model of alternate foot placement. We demonstrate some novel properties of the model, thus showing its predictive value. We give results in a small bipedal walking mechanism. The power of our approach is that it captures key features of human performance and can be easily implemented in most walking machines.

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)0.001
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: none
Teacher disagreement score0.568
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.083
GPT teacher head0.361
Teacher spread0.278 · 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