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Record W2782866311 · doi:10.1515/pjbr-2017-0006

Coordination mechanism for integrated design of Human-Robot Interaction scenarios

2017· article· en· W2782866311 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

VenuePaladyn Journal of Behavioral Robotics · 2017
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsComputer scienceRobotHuman–computer interactionTeleoperationHumanoid robotMechanism (biology)Human–robot interactionFocus (optics)Mechanism designArtificial intelligenceSimulation

Abstract

fetched live from OpenAlex

Abstract The ultimate long-term goal in Human-Robot Interaction (HRI) is to design robots that can act as a natural extension to humans. This requires the design of robot control architectures to provide structure for the integration of the necessary components into HRI. This paper describes how HBBA, a Hybrid Behavior-Based Architecture, can be used as a unifying framework for integrated design of HRI scenarios. More specifically, we focus here on HBBA’s generic coordination mechanism of behavior-producing modules, which allows to address a wide range or cognitive capabilities ranging from assisted teleoperation to selective attention and episodic memory. Using IRL-1, a humanoid robot equipped with compliant actuators for motion and manipulation, proximity sensors, cameras and a microphone array, three interaction scenarios are implemented: multi-modal teleoperation with physical guidance interaction, fetching-and delivering and tour-guiding.

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)0.001
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: none
Teacher disagreement score0.785
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.183
GPT teacher head0.452
Teacher spread0.269 · 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