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Record W2020169422 · doi:10.1299/jsdd.2.463

Numerical Simulations of Level-Ground Walking Based on Passive Walk for Planar Biped Robots with Torso by Hip Actuators

2008· article· en· W2020169422 on OpenAlexfundno aff
Terumasa NARUKAWA, Masaki Takahashi, Kazuo YOSHIDA

Bibliographic record

VenueJournal of System Design and Dynamics · 2008
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceSimon Fraser UniversityInstitut national de recherche en informatique et en automatique (INRIA)
KeywordsTorsoSwingActuatorPlanarRobotSimulationComputer scienceRange (aeronautics)Control theory (sociology)EngineeringArtificial intelligenceAerospace engineeringControl (management)Mechanical engineering

Abstract

fetched live from OpenAlex

This study aims at a design technique of energy-efficient biped walking robots on level ground with simple mechanisms. To do this, we focus on the passive dynamic walkers which can walk stably down a shallow slope without actuators and controllers. On level ground, active walking should be studied because the mechanical energy is mainly lost through the swing-leg impacts with the ground. In this paper, numerical simulations show that planar biped robots with torso can walk efficiently on level ground over a wide range of speed by only using hip actuators. The hip actuators are used for a torso and swing-leg control based on passive-dynamic walking. The torso is used to generate active power replacing gravity used in the case of the passive walk. The swing-leg control is introduced to walk stably over a wide range of speed.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.206
Teacher spread0.188 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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