  Intelligent vision control systems for surgical robots should adapt to
unknown and diverse objects while being robust to system disturbances. Previous
methods did not meet these requirements due to mainly relying on pose
estimation and feature tracking. We propose a world-model-based deep
reinforcement learning framework "Grasp Anything for Surgery" (GAS), that
learns a pixel-level visuomotor policy for surgical grasping, enhancing both
generality and robustness. In particular, a novel method is proposed to
estimate the values and uncertainties of depth pixels for a rigid-link object's
inaccurate region based on the empirical prior of the object's size; both depth
and mask images of task objects are encoded to a single compact 3-channel image
(size: 64x64x3) by dynamically zooming in the mask regions, minimizing the
information loss. The learned controller's effectiveness is extensively
evaluated in simulation and in a real robot. Our learned visuomotor policy
handles: i) unseen objects, including 5 types of target grasping objects and a
robot gripper, in unstructured real-world surgery environments, and ii)
disturbances in perception and control. Note that we are the first work to
achieve a unified surgical control system that grasps diverse surgical objects
using different robot grippers on real robots in complex surgery scenes
(average success rate: 69%). Our system also demonstrates significant
robustness across 6 conditions including background variation, target
disturbance, camera pose variation, kinematic control error, image noise, and
re-grasping after the gripped target object drops from the gripper. Videos and
codes can be found on our project page: https://linhongbin.github.io/gas/.
