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Record W2737790452 · doi:10.1109/icra.2017.7989554

Angular momentum compensation in yaw direction using upper body based on human running

2017· article· en· W2737790452 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAngular momentumHumanoid robotTorsoPhysicsAngular accelerationCompensation (psychology)Computer scienceAngular displacementControl theory (sociology)Angular velocityClassical mechanicsRobotArtificial intelligenceAcousticsAnatomyControl (management)Psychology

Abstract

fetched live from OpenAlex

Humans utilize their torsos and arms while running to compensate for the angular momentum generated by the lower-body movement during the flight phase. To enable this capability in a humanoid robot, the robot should have human-like mass, a center of mass position, and inertial moment of each link. To mimic this characteristic, we developed an angular momentum control method using a humanoid upper body based on human motion. In this method, the angular momentum generated by the movement of the humanoid lower body is calculated, and the torso and arm motions are calculated to compensate for the angular momentum of the lower body. We additionally developed the humanoid upper-body mechanism that mimics the human link length and mass property by using carbon fiber reinforced plastic and a symmetric structure. As a result, the developed humanoid robot could generate almost the same angular momentum as that of human through human-like running motion. Furthermore, when suspended in midair, the humanoid robot produced the angular momentum compensation in the yaw direction.

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 score0.115
Threshold uncertainty score0.347

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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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