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On Task-Specific Redundant Actuation of Spring-Assisted Modular and Reconfigurable Robot

2020· article· en· W3094737593 on OpenAlexaff
Christopher Singh, Guangjun Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsModular designRobotTask (project management)Computer scienceActuatorSpring (device)Mode (computer interface)Embedded systemControl engineeringComponent (thermodynamics)EngineeringSimulationArtificial intelligenceHuman–computer interactionSystems engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Modular and reconfigurable robots (MRRs) are unique and highly versatile for their self-innovation potential. Multiple working mode (MWM) on-line control adds potential by enabling each spring-assisted MRR (SA-MRR) joint module to switch independently between primary (motor-only) mode, and secondary, redundant actuation (spring-assisted) mode. In this work we proposed the spring-assisted mode as an on-board, physical innovation aid for uncertain, task-critical manipulation acts in uncontrolled environments. The spring-assisted mode is characterized by synergy of the spring and motor energy, and strengthens the SA-MRR by complementing the net actuation effort to help overcome task failure, to improve competency at ordinary tasks, and to enhance SA-MRR suitability as a tool for new tasks. Spring modules are fully reconfigurable offline through component swapping, and human-robot collaboration safety is considered. Task-specific, physical innovation with the SA-MRR was investigated by applying the spring-assisted mode directly to strenuous task segments in simulation case studies, demonstrating effectiveness of the proposed MWM approach.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.512
Threshold uncertainty score0.381

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 head0.030
GPT teacher head0.204
Teacher spread0.174 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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