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Record W3005386404 · doi:10.1109/lra.2020.2970944

6-DOF Force Sensing for the Master Tool Manipulator of the da Vinci Surgical System

2020· article· en· W3005386404 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 Robotics and Automation Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJoystickSurgical robotHaptic technologySoftwareManipulator (device)Interface (matter)TorqueImpedance controlSimulationEngineeringRobotComputer scienceEmbedded systemArtificial intelligenceOperating systemPhysics

Abstract

fetched live from OpenAlex

We integrated a force/torque sensor into the wrist of the Master Tool Manipulator (MTM) of the da Vinci Standard Surgical system. The added sensor can be used to monitor the surgeon interaction forces and to improve the haptic experience. The proposed mechanical design is expected to have little effect on the surgeon's operative experience and is simple and inexpensive to implement. We also developed a software package that allows for seamless integration of the force sensor into the da Vinci Research Kit (dVRK) and the Robot Operating System (ROS). The complete mechanical and electrical modifications, as well as the software packages are discussed. Two example applications of impedance control at the MTM and joystick control of the PSM are presented to demonstrate the successful integration of the sensor into the MTM and the interface to the dVRK.

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 score0.593
Threshold uncertainty score0.207

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 head0.026
GPT teacher head0.203
Teacher spread0.177 · 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