Bimanual telerobotic surgery with asymmetric force feedback: A daVinci<sup>&#x00AE;</sup> surgical system implementation
Bibliographic record
Abstract
This paper describes the applicability of an asymmetric force feedback control framework for bimanual robot-assisted surgery using the da Vinci surgical system (Intuitive Surgical Inc.). The core idea of this method, previously presented in [1], is that when completing two-handed tasks involving an action and a reaction force, the forces applied on the environment by the action hand are not transferred back to the same hand, but rather to the reaction hand. Such a method provides an intuitive way of feeling the force, while avoiding the instability issues, since the control loop in not closed from the slave to the master of the same hand. In the introductory paper [1], the technique was implemented using game controllers with simple tasks. In this paper, the technique was implemented on the da Vinci surgical system (Classic version) using the da Vinci Research Kit (dVRK) controllers that enable complete access to all control levels of the da Vinci robot manipulators via custom mechatronics and open-source software. The implementation involved a full re-write of a teleoperation controller based on kinematic correspondence with gravity compensation, as well as torque control functions for force rendering on the da Vinci master manipulators. A series of suture knot tying and haptic exploration experiments were conducted in which a small group of users, both surgeons (N=3) and novices (N=6) evaluated the system. The results show that the proposed technique has some promise when implemented in a realistic 14 degrees of freedom system, but further work is necessary to make the system fully usable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".