Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
- Published
- Source
- arXiv
- Paper number
- 882
- Field
- Robotics
- arXiv ID
- 2608.11204
Key points
- The authors propose a two-stage learning method that first learns visual dynamics in surgical scenes from unlabeled endoscopic video and then fine-tunes on a small amount of labeled action data.
- Video pretraining raises the average success rate on four SurRoL benchmark tasks from 63.5 percent to 77.8 percent.
- On PegTransfer, it improves by 20 percentage points, from 66 percent to 86 percent, and the effect is strongest on tasks that involve many contacts and two-handed cooperation.
- The video-pretrained model reaches higher performance with only half the fine-tuning budget, which greatly improves training efficiency.
- The same trend appears on real surgical robot data from JIGSAW, which confirms that the effect is real and not just a simulator artifact.
Paper links
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