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Record W4416202460 · doi:10.1109/toh.2025.3632157

Inertia Compensation Using Flywheels in Parallel Robots for the Assisted Manipulation of Large Payloads

2025· article· en· W4416202460 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Haptics · 2025
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlywheelCompensation (psychology)RobotControl theory (sociology)TorqueInertiaPayload (computing)ActuatorRendering (computer graphics)

Abstract

fetched live from OpenAlex

This paper proposes the use of reaction wheels in parallel mechanisms for physical human-robot interaction during the co-manipulation of large payloads. The concept combines the advantages of a mechanically backdrivable robot - for hands-on-payload interaction - with the reactiveness of flywheels for the compensation of inertial loads, thereby leading to a smooth and low-inertia rendering. In the proposed approach, gravity compensation and dynamic compensation are partitioned and assigned to two subsets of actuators, namely the backdrivable joint actuators and the flywheel actuators, the latter being smaller and properly geared actuators to benefit from faster dynamics for interaction stability purposes. Simulation results of a human interaction with a planar robot to displace a payload show that the desired dynamic behaviour of the moving platform is correctly rendered, while indicating that the inertia compensation torques may vary more quickly than the gravity torques, which supports the proposed idea. Experiments are also conducted to validate the rendering of the desired virtual dynamics to the user.

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)0.000
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 score0.959
Threshold uncertainty score0.363

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 head0.046
GPT teacher head0.292
Teacher spread0.245 · 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