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Record W2978242027 · doi:10.1017/s0263574719001413

Gait Optimization for Quadruped Rovers

2019· article· en· W2978242027 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

VenueRobotica · 2019
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGaitKinematicsTorqueControl theory (sociology)Computer scienceEffect of gait parameters on energetic costStability (learning theory)SimulationEngineeringGait analysisArtificial intelligenceControl (management)Physical medicine and rehabilitationPhysics

Abstract

fetched live from OpenAlex

SUMMARY This paper studies the gait characteristics of a quadruped rover that mimics domestic cats, and attempts to optimize these characteristics. The kinematics and dynamics formulation of the rover’s three-dimensional model is developed, and its gait, pose and corresponding control parameters are computed to minimize torque or maximize speed, using a genetic algorithm. The optimization model consists of a set of equality and inequality constraints that ensure the feasibility and stability of the gaits, while considering the entire gait spectrum that feline species exhibit. The optimal gaits for minimizing the torque closely resemble lateral sequence gaiting, with a trotting behaviour as speed increases. A running gait is obtained at the maximum speed. The optimization results appear to conform to the biological observations of feline species, suggesting the tendency of conserving energy in biological gaiting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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