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Record W2159571890 · doi:10.1109/crv.2008.23

Active Vision for Door Localization and Door Opening using Playbot: A Computer Controlled Wheelchair for People with Mobility Impairments

2008· article· en· W2159571890 on OpenAlexafffund
Alexander Andreopoulos, John K. Tsotsos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWheelchairComputer scienceComputer visionContext (archaeology)Frame (networking)Artificial intelligenceRobotHuman–computer interactionSimulationTelecommunications

Abstract

fetched live from OpenAlex

Playbot is a long-term, large-scale research project, whose goal is to provide a vision-based computer controlled wheelchair that enables children and adults with mobility impairments to become more independent. Within this context, we show how Playbot can actively search an indoor environment to localize a door, approach the door, use a mounted robotic arm to open the door, and go through the door, using exclusively vision-based sensors and without using a map of the environment. We demonstrate the effectiveness of active vision for localizing objects that are too large to fall within a single camerapsilas field of view and show that well-calibrated vision-based sensors are sufficient to safely pass through a door frame that is narrow enough to tolerate a wheelchair localization error of at most a few centimetres. We provide experimental results demonstrating near perfect performance in an indoor environment.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2008
Admission routes2
Has abstractyes

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