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Record W2160571601 · doi:10.1109/robio.2004.1521869

Collective Sorting with Multiple Robots

2005· article· en· W2160571601 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
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSortingComputer scienceRobotTask (project management)Process (computing)Object (grammar)PaceConvergence (economics)Control (management)Artificial intelligenceSimple (philosophy)Distributed computingAlgorithmEngineering

Abstract

fetched live from OpenAlex

Inspired by the behavior of social insects, we tackle the problem of sorting objects with a group of robots under the control of reactive behaviors. Our control algorithm is based on earlier studies of this problem, but depends on more sensing than previous minimalist solutions. With additional sensing information and our simple behavioral rules, we empirically demonstrate that our control algorithm is able to create a complete separation of objects of two different classes. Through simulation, we also show the robust convergence of the sorting process, which previous algorithms could not achieve. This result is independent of the number of robots participating in the task, the initial configuration of the world, and the number of objects to be sorted. We also show indirectly that sorting is not a strictly cooperative task in the sense that even a single robot is capable of performing the task, though at a reduced pace. Finally, we present a model that characterizes the growth of object clusters, which can be used to understand the dynamics of the sorting process

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.251

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)0.0000.000

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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

Citations11
Published2005
Admission routes1
Has abstractyes

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