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Record W4233452784 · doi:10.32920/ryerson.14647371

Advanced human-robot interaction system based on human communication recognition

2021· preprint· en· W4233452784 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
Typepreprint
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHuman–computer interactionRobotHuman–robot interactionComputer scienceGestureGesture recognitionSocial robotMobile robotFocus (optics)Artificial intelligencePersonal robotHumanoid robotRobot control

Abstract

fetched live from OpenAlex

This work describes the implementation of various human-robot interaction systems in a functioning mobile robot. This project is the result of integrating a tracking system for human faces and objects, face recognition, gesture recognition, body tracking, stereo-vision, speech synthesis, and voice recognition. The majority of these systems are custom designed for this particular project, these systems and how they were designed are explained in detail throughout this report. A unique vector-based approach is used for gesture recognition. There is minimal focus on the mechanics and electronics of the human-robot interaction system, but rather on the information processing of the robot. Using combinations of many information processing systems will allow robots to interact with human users more naturally, and will provide a natural conduit for future cooperative human-robot efforts. This project lays the groundwork for what will be a large collaborative effort aimed at creating possibly one of the most advanced human interactive robot in the world.

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 categoriesMeta-epidemiology (narrow)
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 score0.472
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.060
GPT teacher head0.303
Teacher spread0.242 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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