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Record W4407822325 · doi:10.3103/s0147688224700357

STRL Robotics: Intelligent Control for Robotic Platform in Human-Oriented Environment

2024· article· en· W4407822325 on OpenAlexaff
Konstantin Mironov, Dmitry Yudin, Muhammad Alhaddad, D. A. Makarov, Daniil Pushkarev, Sergey Linok, Ilya Belkin, A. S. Krishtopik, В. А. Головин, Konstantin Yakovlev, Aleksandr I. Panov

Bibliographic record

VenueScientific and Technical Information Processing · 2024
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsRoboticsArtificial intelligenceComputer scienceRobotic paradigmsHuman–computer interactionControl (management)Systems engineeringRobotEngineering

Abstract

fetched live from OpenAlex

This article considers the problem of synthesizing the behavior of mobile robotic manipulator when solving tasks in a human-oriented environment. The architecture of the control system is presented, which integrates the modules responsible localization and mapping in an original way, planning the motion of the mobile platform between given points, controlling movement along the planned path, using object recognition on sensor data, and controlling the manipulator when interacting with recognized objects. These components are implemented for an example task of ensuring the mobility of a robotic system in a multifloor office building equipped with elevators. During the experiments, the implemented set of components allowed a real robotic system to use the elevator of an office building. The program code is published in the public domain and is available at the link https://github.com/cds-mipt/strl_robotics .

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.275
Teacher spread0.252 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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