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Record W2027065854 · doi:10.1109/icra.2014.6907115

Push recovery and online gait generation for 3D bipeds with the foot placement estimator

2014· article· en· W2027065854 on OpenAlex
Brandon J. DeHart, Dana Kulić

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 Waterloo
Fundersnot available
KeywordsHumanoid robotGaitRobotComputer scienceWork (physics)EstimatorRest (music)SimulationGround reaction forceRobot kinematicsKey (lock)TrajectoryControl theory (sociology)Physical medicine and rehabilitationMobile robotControl (management)EngineeringKinematicsArtificial intelligenceMathematicsComputer security

Abstract

fetched live from OpenAlex

Humanoid robots have many potential applications in man-made environments, including performing hazardous tasks, assisting the elderly, and as a replacement for our aging workforce. However, generating a reliable gait for biped robots is challenging, particularly for dynamic gait and in the presence of unknown external disturbances, such as a bump from someone walking by. In this work, a 3D formulation of the Foot Placement Estimator is used with a high-level control strategy to achieve a dynamic gait capable of handling external disturbances. A key benefit of this approach is that the robot is able to respond in real time to external disturbances regardless of whether it is at rest or in motion. This strategy is implemented in simulation to control a 14-DOF lower-body humanoid robot being subjected to unknown external forces, both when at rest and while walking, and shown to generate stabilizing stepping actions.

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 score0.911
Threshold uncertainty score0.158

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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