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REVIEW 3 major objections 5 minor 52 references

Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Two vision-based reinforcement-learning agents trained in a fluid simulator transfer to a physical da Vinci Research Kit and autonomously perform surgical irrigation and suction, leaving contaminant residuals close to those of a human…

desk verdict Credible first demonstration of autonomous irrigation-suction with raw RGB on a dVRK, but the residual-mass numbers are undercut by an unresolved handling of force-terminated trials. read the letter →

arxiv 2411.14622 v2 pith:7QZYFJOD submitted 2024-11-21 cs.RO

Yafei Ou , Mahdi Tavakoli This is my paper · ORCID
classification cs.RO
keywords surgicalroboticsirrigation-suctionreinforcementlearningsim-to-realtransferfluidsimulationdomainrandomizationvision-basedcontroldaVinciResearchKit
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to establish that the two-step surgical irrigation-suction routine—rinsing contaminants and then vacuuming the fluid away—can be automated end-to-end by reinforcement-learning agents that see only raw RGB camera images, and that these agents will work on a physical da Vinci Research Kit after training in simulation. The authors train separate vision-based policies for irrigation and suction in a custom simulator with visually plausible fluid mixing, then transfer them to the real robot using domain randomization, a two-lesson curriculum, and imitation learning. In real-world trials, the irrigation agent followed by manual suction leaves roughly 2.1-2.2 grams of an initial ~5.5 grams of contaminant, close to the 1.90 grams left by manual human irrigation, while the suction agent leaves 2.64 and 2.24 grams from larger starting volumes. Fully autonomous irrigation followed by autonomous suction leaves an average of 2.42 grams of suctionable liquid behind, which the authors find is not statistically different from suction alone. If correct, this would be the first automated, feature-free demonstration of both fluid-handling subtasks in robot-assisted surgery, with the practical consequence of reducing the surgeon's workload during cleaning.

What carries the argument

The load-bearing machinery is CRESSim-ML, the paper's simulated surgical-robot learning platform, plus a trained policy pair. The simulation side uses position-based fluid dynamics (PBF) particles with a screen-space rendering pass and a color-diffusion scheme that updates each particle's color as a distance- and speed-weighted average of nearby particles, so irrigated liquid visibly mixes with the contaminant. The control side exploits the fact that the patient-side manipulator with the suction/irrigator tool is a kinematically constrained 5-DoF robot: the policy outputs incremental joint-space targets, avoiding the need for Cartesian inverse kinematics, and observes current joint positions plus a tissue-contact indicator. The transfer side combines domain randomization over colors, lighting, camera pose, tissue shape, and fluid physics with a two-lesson curriculum for irrigation and imitation learning (behavior cloning and a GAIL-style auxiliary reward) that helps the suction agent but not the irrigation one. Together these components let a raw-RGB policy trained in simulation produce the reported physical-world results.

What would settle it

Re-run the suction-only and combined irrigation-suction trials on the physical robot, this time including the nine force-terminated trials in the average as their measured or worst-case residuals; if the mean suctionable residue then rises well above the 2.24-2.64 gram band, the transfer claim is only true for a selected subset of trials. A complementary check is to vary the camera pose, light angle, or contaminant color beyond the trained randomization range and measure the residual after autonomous irrigation and manual suction.

Watch

Extended reading notes

Core claim

Stated on the paper's own terms: two agents, one for irrigation and one for suction, map an $84 \times 84$ RGB frame plus stacked joint positions to incremental 5-DoF joint-space commands at 10 Hz, and after sim-to-real transfer they complete the physical irrigation-suction procedure in most trials. The irrigation agent learns to aim the suction/irrigator at dark contaminant and toggle irrigation only while the tool is close to it, using a curriculum that first teaches approach-and-activate behavior before adding the full dilution reward. The suction agent learns to move toward liquid regions and remove them under a cone-shaped suction force field, with rewards for particles removed and for approaching the nearest liquid. Real-world measurements put irrigation-plus-manual-suction residuals at about 2.1-2.2 grams versus 1.90 grams for human irrigation, suction-only residuals at 2.64 and 2.24 grams for roughly 22-gram and 31-gram starting pools, and combined autonomous trials at 2.42 grams of suctionable liquid left behind out of an initial ~5 grams, with 4.40 grams total remaining because the suction policy leaves some irrigated liquid. An ANOVA finds no statistically significant difference among the three suction-performance groups, and the paper reports no significant correlation between irrigation quality and suction outcome.

Load-bearing premise

The reported real-world averages come from completed trials only; the performance figures assume that the nine trials halted by the hard-coded force limit would not have added substantially larger residuals had they been allowed to finish.

Editorial extensions

If this is right

  • A template for automating fluid-handling surgical subtasks becomes available: simulate with PBF and color diffusion, randomize appearance and physics, and train a raw-RGB joint-space policy.
  • The suction agent can be deployed independently, for example in blood-suction tasks, because it was trained on diverse liquid configurations and does not require the irrigation agent.
  • In the authors' statistical test, the fully autonomous two-step sequence performs no worse than suction alone, meaning an upstream autonomous irrigation step does not measurably harm downstream suction.
  • The measured irrigation gap to human operation is about 0.2-0.3 grams, which the paper presents as showing agent performance close to that of a human.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that a high-level supervisor could decide when to switch from irrigation to suction and when to repeat the cycle; the 2.42 grams of leftover suctionable liquid suggests a second pass would reduce the residual further.
  • The nine force-limit terminations imply that contact-rich suction needs an explicit recovery behavior, such as lifting the end-effector off the tissue before repositioning, which the current policy lacks.
  • The color-diffusion and screen-space fluid rendering could be reused for vision-based training in other mixed-fluid surgical contexts, such as bleeding with cauterization smoke, where visual mixing matters more than exact fluid mechanics.
  • Adding depth or stereo observations would likely shrink the reported sim-to-real gap, but the paper's exclusion of depth is a reasonable design choice because real-world depth is noisy; this trade-off is testable.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This manuscript proposes CRESSim-ML, a Unity/PhysX-based learning platform for the da Vinci Research Kit, and uses it to train two vision-based RL agents—one for irrigating a contaminant and one for suctioning liquid—using domain randomization, curriculum learning, and imitation learning. The agents are transferred to a physical dVRK with an EndoWrist Suction/Irrigator and evaluated in irrigation-only (10 trials), suction-only (20 trials), and combined irrigation-suction (10 trials) settings. The headline quantitative claims are that after autonomous irrigation plus manual suction, 2.11 g (abstract: 2.21 g) of contaminant remains versus 1.90 g for manual irrigation, that the suction agent leaves 2.64 and 2.24 g residuals for roughly 20 g and 30 g initial fluid loads, and that combined autonomous irrigation-suction leaves 4.40 g total weight with 2.42 g of suctionable residual. The authors position this as the first automated two-step irrigation-suction policy using raw RGB observations.

Significance. If the empirical results survive scrutiny, this is a useful advance: it extends sim-to-real RL to fluid manipulation in surgery, demonstrates a two-step irrigation-suction pipeline with raw RGB input, and provides a reusable simulation platform. The authors deserve credit for conducting physical experiments with external weight measurements rather than only simulated metrics, for reporting training curves and reward design, and for documenting suboptimal outcomes. The main novelty is the simulation and application contribution; the RL machinery (PPO plus DR/CL/IL) is standard. The quantitative strength of the paper is currently limited by small per-condition sample sizes, the unresolved treatment of force-terminated trials, and an internal inconsistency in the headline irrigation number.

major comments (3)
  1. [Section VII.C; Sections VI.C and VI.D] Section VII.C states that 9 of the 30 suction trials (20 suction-only plus 10 combined) were terminated by a hardcoded EE force limit, but Sections VI.C and VI.D report residual means (2.64 +/- 1.87 g, 2.24 +/- 2.24 g, and 2.42 +/- 2.04 g) without stating whether terminated trials were included, excluded, or assigned a terminal weight. Force termination occurs when the EE contacts tissue or liquid, which is exactly the situation in which pooled fluid must be suctioned, so excluding those trials conditions the reported averages on success and likely biases them downward. If the trials were included, the measurement at termination is not defined. This ambiguity is load-bearing for the sim-to-real transfer claim; the authors should report all per-trial outcomes, state the inclusion rule, and provide the mean over all 30 trials under a clearly defined terminal measurement.
  2. [Abstract vs. Section VI.B] The abstract reports that autonomous irrigation leaves 2.21 g after manual suction, while Section VI.B reports 2.11 +/- 0.80 g for the same quantity; one of these values is incorrect, and the inconsistency affects a headline result. In addition, the claim that the agent's performance is 'close to that of a human' (1.90 +/- 0.49 g) is based on overlapping uncertainties with no significance test or confidence interval. With n=10, the difference of 0.21 g is not interpretable as equivalence; report an appropriate statistical comparison or soften the claim to a qualitative observation.
  3. [Sections V.A and VI.B] The irrigation-only evaluation measures performance by the contaminant weight remaining after the agent's irrigation followed by a manual suction that is assumed to be optimal. This is an indirect proxy for 'dilution', and the assumption of optimal manual suction is neither validated nor described (number of passes, suction duration, stopping rule). Since the trained irrigation reward is based on simulated particle states, no real-world measurement directly confirms that the agent is diluting the contaminant rather than merely moving fluid around. The proxy is reasonable as a first-order metric, but it should be explicitly framed as a proxy and the manual-suction protocol should be specified so that the irrigation claim is reproducible.
minor comments (5)
  1. [Figure 11b] Figure 11b would be much more informative if force-terminated trials were marked with a distinct symbol, since the current plot is used to support the residual means and the treatment of terminated trials is under discussion.
  2. [Section VI.D] The Pearson and Spearman correlations are computed on n=10 trials; the absence of significant correlation is weak evidence and should be reported as such rather than as support for independence of irrigation and suction performance.
  3. [Section VI.D and Section IX] There are typos in the manuscript: 'ANOV A' should be 'ANOVA' in Section VI.D, and the acknowledgment heading is printed as 'A CKOWLEDGMENTS'.
  4. [Equation (2)] Equation (2) and the adjacent text describe color diffusion with weights that include a velocity factor c||v_j||, but the notation does not make clear whether the weights are normalized over both i and j or only j; clarifying this would make the update well-defined and reproducible.
  5. [Section VII.A] The statement that no quantitative analysis of sim-to-real transfer was performed is an important limitation; it should be reflected more prominently in the conclusion and not only in the discussion.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the central sim-to-real claims are validated by external real-world weight measurements, not by the training rewards or self-cited components.

full rationale

The paper's derivation chain is not circular. Two vision-based RL agents are trained in a simulator with hand-crafted rewards, domain randomization, curriculum learning, and imitation learning; the headline results are grams of residual liquid measured on a scale in the real world after autonomous or manual suction. These outcome metrics are external to the training objective and are not constructed from the reward features (Table I) or from the simulator state. The suction environment borrows its cone-force suction model and reward structure from the authors' prior work [19], and the CRESSim-ML platform builds on CRESSim [22]; these are self-citations, but they are building blocks used to construct the training environment, not conclusions that the present paper tries to derive from them. The real-world trials are an independent, externally falsifiable check of the sim-to-real transfer claim, so the citations do not bear the weight of the empirical conclusion. The paper's own Section VII.C discloses a potential confound: 9 suction trials were terminated by a hardcoded force limit, and the reported means do not state whether those trials are included. That is a statistical-reporting weakness, not a circularity: it does not make the reported residual mass identical to any input or fitted quantity. The irrigation evaluation uses manual suction after autonomous irrigation as a proxy for irrigation effectiveness; this is an explicit measurement assumption, not a self-referential derivation. No equation in the paper defines a prediction in terms of the data it is meant to predict.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The paper introduces no new physical entities. It does introduce a software platform, CRESSim-ML, but that is a reusable simulator, not a postulated particle, force, or dimension. The central claims instead rest on hand-tuned reward weights, unspecified randomization ranges, and several explicit modeling simplifications listed above.

free parameters (6)
  • Reward function weights for irrigation and suction (Table I) = e.g., 10 for distance-to-blood change, 5 for task completion, 0.2/0.03 for particle removal rewards
    Section III.C states training trials were conducted to tune the weights for ideal performance; these hand-tuned values directly shape the learned policy.
  • Color diffusion kernel parameters sigma and velocity factor c = Not quantified in the paper
    Equation (2) uses exp(-||pi-pj||/2 sigma^2) times c*||vj||; the constants are chosen by hand for visual plausibility and affect what the vision-based agent sees.
  • Threshold for determining whether dense blood particles are affected by irrigation = Not stated
    Section III.C1 uses an unspecified threshold to decide task completion and the completion reward; this directly influences irrigation training targets.
  • Domain randomization ranges for object colors, camera pose, lighting, and physics properties = Not stated numerically
    Section IV.A randomizes these within 'a specified range' chosen from real-world images; the exact ranges are not reported and are needed to reproduce the trained behavior.
  • Curriculum lesson transition proportions for irrigation = Not stated numerically
    Section IV.A describes transitional lessons sampled 'according to a proportion' between task configurations; the schedule is not specified.
  • Hardcoded force limit threshold for real-world trials = Not stated
    Section VII.C imposes a force limit that terminated 9 trials; the numeric threshold is not reported and the handling of terminated trials is not described.
assumptions (7)
  • domain assumption PhysX 5 position-based fluids provide a sufficiently faithful model of irrigation and suction fluid behavior for sim-to-real transfer.
    Section III.A builds the entire training environment on PBF; if the fluid model is not faithful, the learned policy will not transfer.
  • domain assumption Screen-space fluid rendering with surface-only color extraction produces images close enough to real camera images for a vision policy to transfer.
    Section III.A2 calls the simplification 'valid without losing much visual plausibility'; this is asserted, not measured.
  • domain assumption A manual suction performed after autonomous irrigation removes all suctionable fluid, so the remaining weight isolates irrigation performance.
    Section V.A states the workaround and explicitly assumes manual suction is optimal; if incomplete, the irrigation-only numbers are not comparable.
  • domain assumption Tomato ketchup, playdough, and dyed water adequately emulate clotted blood, tissue, and diluted blood for validating the agents.
    Sections V.A-C use ketchup, playdough, and red-colored fluid; Section III.C1 also notes red irrigant is a simplification. The setup is not intended to represent a specific surgical procedure.
  • domain assumption The rigid-body tissue approximation does not critically break the learned policy in the real world.
    Section VII.B trades off FEM soft tissue for speed and acknowledges missing viscoelasticity, permeability, and surface tension; the implication is these are acceptable for transfer, but this is not demonstrated.
  • domain assumption The cone-shaped suction force field from [19] is an adequate model of real suction for policy transfer.
    Section III.C2 implements the suction model from [19], while Section VII.A admits that real suction requires contact with the liquid surface and is pressure-based, unlike the simulation.
  • ad hoc to paper Trials terminated by the hardcoded force limit can be ignored or excluded without biasing the reported averages.
    Section VII.C reports 9 terminated suction trials but never states how they enter the statistics; the paper's summary numbers appear to describe only completed trials.

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Cite this review

Pith. "Pith review of Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning." pith.science (2026). https://pith.science/paper/7QZYFJOD

@misc{pith2026241114622,
  author       = {Pith},
  title        = {Pith review of: Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7QZYFJOD}},
  note         = {Machine review of arXiv:2411.14622}
}
read the original abstract

The irrigation-suction process is a common procedure to rinse and clean up the surgical field in minimally invasive surgery (MIS). In this process, surgeons first irrigate liquid, typically saline, into the surgical scene for rinsing and diluting the contaminant, and then suction the liquid out of the surgical field. While recent advances have shown promising results in the application of reinforcement learning (RL) for automating surgical subtasks, fewer studies have explored the automation of fluid-related tasks. In this work, we explore the automation of both steps in the irrigation-suction procedure and train two vision-based RL agents to complete irrigation and suction autonomously. To achieve this, a platform is developed for creating simulated surgical robot learning environments and for training agents, and two simulated learning environments are built for irrigation and suction with visually plausible fluid rendering capabilities. With techniques such as domain randomization (DR) and carefully designed reward functions, two agents are trained in the simulator and transferred to the real world. Individual evaluations of both agents show satisfactory real-world results. With an initial amount of around 5 grams of contaminants, the irrigation agent ultimately achieved an average of 2.21 grams remaining after a manual suction. As a comparison, fully manual operation by a human results in 1.90 grams remaining. The suction agent achieved 2.64 and 2.24 grams of liquid remaining across two trial groups with more than 20 and 30 grams of initial liquid in the container. Fully autonomous irrigation-suction trials reduce the contaminant in the container from around 5 grams to an average of 2.42 grams, although yielding a higher total weight remaining (4.40) due to residual liquid not suctioned. Further information about the project is available at https://tbs-ualberta.github.io/CRESSim/.

Figures

Figures reproduced from arXiv: 2411.14622 by the authors.

Figure 1
Figure 1. Overall framework. [24]–[26]. Many of these studies leverage RL approaches [1]– [3], [5]–[10], [19]. However, applying RL to surgical tasks involving fluid manipulation, such as irrigation and suction, remains relatively underexplored due to the challenges and limited software tools for simulating fluid dynamics. B. Surgical Simulation and Sim-to-Real Transfer Surgical task simulation is challenging compared with da… view at source ↗
Figure 2
Figure 2. Particle color diffusion. (a) Initial colors; (b) Colors after 10 steps. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 5
Figure 5. A surgical robot learning platform for the dVRK with Unity, ML [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: (a) Simulated PSM with the EndoWrist One Suction/Irrigator; (b) [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Simulated training environments. (a) Overview of the scene setup; (b) [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Examples of training environments with domain randomization for [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Unity scene consisting of 16 training areas with random parameters. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 11
Figure 11. Figure 11: Results from real-world trials for (a) irrigation only and (b) suction [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]
Figure 10
Figure 10. Figure 10: Learning curves for (a) irrigation and (b) suction. Exponentially [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 12
Figure 12. Figure 12: (a) Experimental procedure and (b) results for real-world combined [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]
Figure 13
Figure 13. Figure 13: Representative snapshots selected at different action steps from real-world irrigation experiments. [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]
Figure 14
Figure 14. Figure 14: Representative snapshots selected at different action steps from real-world suction experiments. [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]
Figure 15
Figure 15. Figure 15: Representative snapshots selected at different action steps from complete irrigation-suction experiments in the real world. [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]
Figure 16
Figure 16. Figure 16: Suboptimal outcomes in the real-world trials. [PITH_FULL_IMAGE:figures/full_fig_p013_16.png]
Figure 17
Figure 17. Figure 17: Image observations for irrigation from (a) the simulated environment [PITH_FULL_IMAGE:figures/full_fig_p013_17.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.