MACAW: Reliable And Efficient Surgical Debridement Using Monocular Adaptive Compact Attention Windows

Ziyang Chen; Shutong Jin; Preethi Satish; Sareena Mann; Cael Magner; Danyal Fer; Omid Mohareri; Gary Guthart; Ken Goldberg

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arXiv · 2609.01961

MACAW: Reliable And Efficient Surgical Debridement Using Monocular Adaptive Compact Attention Windows

Ziyang Chen·Shutong Jin·Preethi Satish·Sareena Mann·Cael Magner·Danyal Fer·Omid Mohareri·Gary Guthart·Ken Goldberg

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Abstract

Augmenting the dexterity of human surgeons has the potential to free them from tedious subtasks. We consider debridement (removal of diseased or dead tissue fragments), which is challenging due to imprecision in spatial perception and cable actuation. We develop an augmented dexterity system for surgical debridement that uses visual servoing to align the cable-driven gripper with the target position in the image plane, and then introduces a novel approach to depth control, MACAW: Monocular Adaptive Compact Attention Windows. Across 100 physical trials using the da Vinci Research Kit (dVRK) robot, camera-frame servoing reduced average gripper position offset from 37 to fewer than 5 pixels within 4 optimization steps, taking an average of only 0.39s. MACAW significantly outperforms procedural and learned VLA baselines, achieving a 93% success rate at 11 seconds per fragment, yielding a throughput of 304 fragments per hour. Extending MACAW to a bimanual debridement setup maintains a 92% success rate at an average of 7 seconds per fragment, increasing the throughput to 473 fragments per hour.

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Ziyang Chen, Shutong Jin, Preethi Satish, Sareena Mann, Cael Magner, Danyal Fer, Omid Mohareri, Gary Guthart, Ken Goldberg. 2026-09-02. MACAW: Reliable And Efficient Surgical Debridement Using Monocular Adaptive Compact Attention Windows. https://arxiv.org/abs/2609.01961

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