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Bipedal Walking Trajectory Generation Using Tchebychev Method

2011· article· en· W2033205747 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

VenueAdvanced engineering forum · 2011
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsTorqueTrajectorySet (abstract data type)Control theory (sociology)RobotComputer scienceQuadratic programmingGaitJoint (building)Biped robotAnkleMathematical optimizationMathematicsEngineeringArtificial intelligencePhysical medicine and rehabilitationControl (management)

Abstract

fetched live from OpenAlex

It is still important and difficult for a biped robot to optimally generate the stable walking trajectory, because the mechanical limitations of the given biped robot should be also considered carefully. In this paper, we assume that different walking trajectories enable to be generated according to various set of weights of torques loaded in each partial joint (e.g., ankle, knee, and hip joint). We present a method for generating various bipedal walking trajectories corresponding to a set of weighted torques. For this purpose, Tchebychev method and sequential quadratic programming are employed to optimize single cost functions consisting in a set of weight torques. Some notations and constraints introduced in [1] are used and modified in this paper.

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 score0.687
Threshold uncertainty score0.962

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 head0.025
GPT teacher head0.232
Teacher spread0.207 · 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