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Reduced Interface Model of Flexible Space Robotic Arms and the Application for Robotic Interaction Tasks

2025· article· W7140123292 on OpenAlexafffund
Xu Dai, Ali Raoofian, József Kövecses, Marek Teichmann

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsCM Labs Simulations (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyNature
KeywordsInterface (matter)RobotSpace (punctuation)Robotic armRoboticsKinematicsTrajectory

Abstract

fetched live from OpenAlex

Dynamic simulation plays a critical role in the design and testing of space robotic systems. However, it becomes challenging when dealing with complex systems or tasks involving interactions among multiple subsystems. In such scenarios, improving simulation efficiency is essential. Techniques like model order reduction and co-simulation can be effective in addressing these challenges. The reduced interface model (RIM) is a recently introduced model order reduction technique that captures the dynamics of the full system within a reduced space of motion of interest. RIM can enhance simulation performance, particularly in model-based co-simulation, making it well-suited for space robotics applications that involve subsystem interactions. When applying RIM to robotic arms with flexible components, special considerations must be taken during its formulation. This paper presents a systematic derivation of the flexible RIM and demonstrates its effectiveness in space robotic interaction tasks.

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 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 score0.980
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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