---
title: "Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.11204v1"
permalink: "https://feed.astralor.com/items/itm_409408d0cf01341cd855486d"
published: "2026-08-11T17:59:13.000Z"
score: 75
channel: "ai"
tags: ["Cosmos Policy","Surgical WAM","World Model","Surgical Robot","Video Pretraining"]
---

# Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning

## AI commentary

> What stands out most here is the elegant idea of replacing expensive action labels with video pretraining, which could be highly inspiring for teams struggling with surgical data collection. That said, simulation gains don't guarantee real-world performance, so the next critical step is validating transfer to clinical data.

## At a glance

Learning reliable surgical manipulation policies is often bottlenecked by scarce action-labeled demonstrations, as teleoperated trajectories like those from dVRK are costly to collect, while endoscopic video is comparatively cheap and abundant. To exploit this, researchers propose Surgical WAM, a unified generative model built on Cosmos Policy that jointly predicts future endoscopic observations and executable robot action chunks. It first learns surgical visual dynamics from action-free video and is then fine-tuned on a fixed action-labeled budget; at deployment, it acts as a closed-loop receding-horizon controller. On four simulated surgical tasks, video pretraining raises the average success rate from 63.5% to 77.8%, including an absolute gain of 20 percentage points on PegTransfer, with the largest gains on contact-rich and bimanual tasks. These results demonstrate that action-free video provides transferable visual dynamics priors, offering a data-efficient path toward scaling up surgical robot learning.

## Key points

- Introduces Surgical WAM, a world-action model built on Cosmos Policy that jointly predicts endoscopic video and surgical robot action chunks.
- Leverages action-free endoscopic video for pretraining visual dynamics priors, significantly reducing reliance on costly action labels.
- Raises average success from 63.5% to 77.8% on four simulated surgical tasks, with an absolute gain of 20 points on PegTransfer.
- Demonstrates video pretraining as a data-efficient avenue for scaling surgical robot learning.

## Full text

Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.g., dVRK) trajectories with synchronized kinematics are costly to collect, while surgical tasks demand precise contact handling, long-horizon reasoning, and bimanual coordination. Endoscopic video is comparatively inexpensive and abundant relative to synchronized video--kinematics trajectories, and a natural way to exploit it is to learn world models of surgical scenes. However, existing surgical world models use video primarily for simulation or policy evaluation, and rarely translate the learned dynamics into closed-loop control. This gap raises our central question: under a fixed budget of action-labeled demonstrations, does action-free video pretraining improve closed-loop surgical manipulation? To answer it, we introduce the Surgical World-Action Model (Surgical WAM), a unified generative model built on Cosmos Policy that jointly predicts future endoscopic observations and executable surgical robot action chunks. Surgical WAM first learns surgical visual dynamics from action-free video and is then fine-tuned on the fixed action-labeled budget; at deployment, it acts as a closed-loop, receding-horizon controller that executes a short prefix of each predicted action chunk and replans from the resulting observation. On a suite of four simulated surgical manipulation tasks, video pretraining improves the average success rate from 63.5% to 77.8%, including an absolute gain of 20 percentage points on PegTransfer, with the largest improvements on contact-rich and bimanual tasks. These results demonstrate that action-free video provides transferable visual dynamics priors for learning surgical robot control with limited action supervision, positioning data-efficient video pretraining as a practical path toward scaling up surgical robot learning.

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