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Record W2102873222 · doi:10.1109/robot.2007.364142

Stable Non-linear Force/Position Mapping for Enhanced Telemanipulation of Soft Environments

2007· article· en· W2102873222 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

VenueProceedings - IEEE International Conference on Robotics and Automation/Proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTeleoperationControl theory (sociology)PassivityHaptic technologyComputer scienceNonlinear systemNyquist stability criterionPosition (finance)TorqueLyapunov stabilityRobotMathematicsEngineeringSimulationArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

The performance index in bilateral teleoperation, transparency, is often defined as linear scaling between the master and slave positions, as well as the operator and environment forces. Motivated by applications involving soft tissue manipulation such as robotic surgery, the transparency objective is generalized to include monotonic nonlinear mappings between the master/slave position and force signals. Modified Lyapunov-based adaptive motion/force controllers are presented that can guarantee the convergence of position and force tracking errors in the presence of dynamic uncertainty. Given a priori known bounds on the unknown operator and environment mass-spring-damper parameters, the closed-loop stability is analyzed using an off-axis circle criterion and the Nyquist envelope of interval plant systems. This approach produces far less conservative stability margins than those achievable by the passivity analysis. Experimental results with a two-axis teleoperation setup are provided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.035
GPT teacher head0.266
Teacher spread0.231 · 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