MétaCan
Menu
Back to cohort
Record W2130514525 · doi:10.1109/iros.1995.525877

A control strategy for stable passive running

2002· article· en· W2130514525 on OpenAlexaff
Mojtaba Ahmadi, M. Buehler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsControl theory (sociology)RobotActuatorLegged robotController (irrigation)Computer scienceMotion controlSpring (device)Power (physics)Range (aeronautics)Motion (physics)Energy (signal processing)SimulationEngineeringControl (management)PhysicsArtificial intelligenceStructural engineering

Abstract

fetched live from OpenAlex

We present a control strategy for a simplified model of a one legged running robot which features compliant elements in series with hip and leg actuators. Proper spring selection and initial conditions result in "passive dynamic" operation close to the desired motion, without any actuation. However, this motion is not stable. The proposed controller is based on online calculations of the desired passive dynamic motion and stabilises any fixed robot speed. It also tracks large changes in desired robot velocity and remains largely passive for a wide range of velocities, despite a fixed set of springs, masses and inertias. To this end the desired motion is expressed as a function of a normalized "locomotion time" parameter. Comparisons of simulated runs with direct hip actuation show dramatic energy savings of 95% at 3m/s. Such energy savings are critical for the power autonomy of electrically actuated legged robots.

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)0.000
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.203
Teacher spread0.185 · 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

Citations23
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicRobotic Locomotion and ControlFrench-language works237,207