SRT-H: A Hierarchical Framework for Autonomous Surgery via Language
  Conditioned Imitation Learning
Authors: name: Ji Woong Kim, name: Juo-Tung Chen, name: Pascal Hansen, name: Lucy X. Shi, name: Antony Goldenberg, name: Samuel Schmidgall, name: Paul Maria Scheikl, name: Anton Deguet, name: Brandon M. White, name: De Ru Tsai, name: Richard Cha, name: Jeffrey Jopling, name: Chelsea Finn, name: Axel Krieger

Research on autonomous surgery has largely focused on simple task automation
in controlled environments. However, real-world surgical applications demand
dexterous manipulation over extended durations and generalization to the
inherent variability of human tissue. These challenges remain difficult to
address using existing logic-based or conventional end-to-end learning
approaches. To address this gap, we propose a hierarchical framework for
performing dexterous, long-horizon surgical steps. Our approach utilizes a
high-level policy for task planning and a low-level policy for generating robot
trajectories. The high-level planner plans in language space, generating
task-level or corrective instructions that guide the robot through the
long-horizon steps and correct for the low-level policy's errors. We validate
our framework through ex vivo experiments on cholecystectomy, a
commonly-practiced minimally invasive procedure, and conduct ablation studies
to evaluate key components of the system. Our method achieves a 100\% success
rate across eight unseen ex vivo gallbladders, operating fully autonomously
without human intervention. This work demonstrates step-level autonomy in a
surgical procedure, marking a milestone toward clinical deployment of
autonomous surgical systems.