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Record W2144248494 · doi:10.1109/robot.2003.1241947

Enabling real-time full-body imitation: a natural way of transferring human movement to humanoids

2004· article· en· W2144248494 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
TopicHuman Motion and Animation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHumanoid robotInverse kinematicsComputer visionKinematicsComputer scienceRobustness (evolution)RobotArtificial intelligenceImitationMovement (music)Robot kinematicsHuman–robot interactionHuman–computer interactionMobile robotPsychologyAcoustics

Abstract

fetched live from OpenAlex

We seek intuitive, efficient ways to create and direct human-like behaviors for humanoid robots. Here we present a method to enable humanoid robots to acquire movements by imitation. The robot uses 3D vision to perceive the movements of a human teacher, and then estimates the teacher's body postures using a fast full-body inverse kinematics method that incorporates a kinematic model of the teacher. This solution is then mapped to the robot and reproduced in real-time. The robustness of the method is tested on a 30-degree-of-freedom Sarcos humanoid robot located at ATR using 3D vision data from external cameras and from head-mounted cameras.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.548

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)0.0010.000

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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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

Citations78
Published2004
Admission routes1
Has abstractyes

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