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Record W2155446094 · doi:10.1109/tsmcb.2006.876818

On Impact Dynamics and Contact Events for Biped Robots via Impact Effects

2006· article· en· W2155446094 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

VenueIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2006
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of ManitobaMcGill University
Fundersnot available
KeywordsKinematicsSlippageParametric statisticsSwingComputer scienceRobotControl theory (sociology)Event (particle physics)SimulationContact forceDynamics (music)Control (management)MathematicsEngineeringPhysicsArtificial intelligenceStructural engineeringMechanical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

In this paper, impact dynamics of a five-link biped walking on level ground are studied, and the results are used to correlate the gait parameters with the contact event following impact. The conventional five-link biped kinematic model is improved such that, for the first time, the explicit solution for the external impulses is obtained in a detailed but compact form. Such a solution reveals that the direction of the swing tip velocity prior to impact is a key factor dictating the upcoming contact event and the slippage. The conventional conditions to warrant the types of impact are expanded to make them sufficient. The aforementioned results are used in the parametric analysis to predict the contact event after impact. Such a prediction is important for proper dynamic modeling, motion planning, and control of the upcoming supporting phase.

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 categoriesMeta-epidemiology (narrow)
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 score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.220
Teacher spread0.215 · 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