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Record W2743047736 · doi:10.5430/air.v6n2p100

Using simplified geometric models in skill-based manipulation for objects used in daily life

2017· article· en· W2743047736 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArtificial Intelligence Research · 2017
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsTask (project management)Computer scienceRobotMotion (physics)Geometric modelingReliability (semiconductor)Artificial intelligenceHuman–computer interactionComputer visionEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Recently many types of robots have been playing an active part in various fields. The operation of these robots’ manipulators is an important subject of research. The tasks of manipulation can be regarded as sequences of several motion primitives called “skills”. Skills also have ability to compensate for errors both in modeling and in execution. Data may occur in the elements of the shapes, positions and orientations of objects that can be dispensed with to make geometric models simpler. In order to achieve tasks with high reliability, this paper proposes simplified geometric models based on skill techniques not only for industrial products but also for objects used in the daily life of humans. As examples of simplified geometric models of objects used in daily life, simplified models in a transfer task of plastic bottles and in a transfer task of a cup are explained.

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)0.001
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 score0.504
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.412
GPT teacher head0.438
Teacher spread0.025 · 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