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Record W2116617226 · doi:10.1109/robot.1988.12095

Extending the RCCL programming environment to multiple robots and processors

2003· article· en· W2116617226 on OpenAlexaff
John E. Lloyd, Mike Parker, R. McClain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsMcGill University
Fundersnot available
KeywordsUnixRobotComputer scienceSet (abstract data type)Interface (matter)Programming languageOperating systemEmbedded systemArtificial intelligenceSoftware

Abstract

fetched live from OpenAlex

The Robot Control C Library (RCCL) is a system for developing robot control programs in a UNIX environment, and has proven to be useful in research applications. Work undertaken in expanding RCCL to handle multiple robots, and upgrading its implementation to a multiple-processor microVAX/UNIX system is reported. This includes (1) reworking the primitive set to allow for the specification of multiple robot actions; (2) modifying the trajectory generation mechanism so that several robots may be controlled and coordinated at once; and (3) redesigning the system interface on top of which RCCL is built to allow the creation of multiple real-time robot control tasks interfaced to UNIX.>

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0210.015

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 head0.014
GPT teacher head0.202
Teacher spread0.187 · 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 designBench or experimental
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

Citations57
Published2003
Admission routes1
Has abstractyes

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