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Toward a dynamic analysis of bipedal robots inspired by human leg muscles

2018· article· en· W2883092218 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJOURNAL OF MECHANICAL ENGINEERING AND SCIENCES · 2018
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
FundersUniversiti Malaysia PahangWaseda UniversityUniversité Laval
KeywordsRobotLeg muscleComputer scienceArtificial intelligencePhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

A walking bipedal robot by adding passive springs like mono and biarticular muscles which correspond to rectus femoris (RF), biceps femoris (BF), gastrocnemius (GAS) and tibialis anterior (TA) in human legs has been modeled in this paper. The stability of human-like leg walking can be achieved by adding these passive springs in the leg mechanism of bipedal robot. On the other hand, using these springs may inflict a fatigue during walking due to the additional work that can provide to the joints. The main objective of this paper is to analyze the total work of the robot during walking by proposing four cases of the preload of the springs at the equilibrium position. It’s found from this study that the case that has the most energy saving and ensure the comfortable walking to the robot is when the two muscles (GAS) and (TA) are not preloaded to support the total weight at the equilibrium position.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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