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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 OpenAlexaff
Xiuping Mu, Qiong Wu

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.006
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

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 designSimulation or modeling
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

Citations39
Published2006
Admission routes1
Has abstractyes

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