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Record W2119400628 · doi:10.1109/mhs.2002.1058044

Perception driven robotic assembly based on ecological approach

2003· article· en· W2119400628 on OpenAlexfundno aff
Kiyoharu Tagawa, K. Konishi, Daisuke Itô, Hiromasa Haneda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsRobotAffordancePerceptionAction (physics)Computer scienceSet (abstract data type)Artificial intelligenceGenerator (circuit theory)Robot controlAutonomous robotHuman–computer interactionMobile robotPsychology

Abstract

fetched live from OpenAlex

The key difficulty in robotic assembly is not the control problem but the problem of perception. In this paper, from the viewpoint of the affordance theory, an action-perception cycle model is employed for realizing a perception driven assembly robot. The proposed robot has neither a set of rational action rules nor a map of the environment. However, the robot achieves assembling through the interaction with its surroundings. Instead of action rules, the robot uses a kind of oscillator, which is named action pattern generator (APG), to take active actions. In order to improve the performance of the assembly robot, two types of memory, namely, working memory and episodic memory, are also introduced into the brain of the robot. Simulations show that the assembly robot mates two parts rapidly even though it does not know their initial arrangements.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
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 integrity0.0000.000
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 head0.028
GPT teacher head0.231
Teacher spread0.203 · 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 teacher head, not a consensus.

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

Citations1
Published2003
Admission routes1
Has abstractyes

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