Diffusion Stabilizer Policy for Automated Surgical Robot Manipulations
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Intelligent surgical robots have the potential to revolutionize clinical
practice by enabling more precise and automated surgical procedures. However,
the automation of such robot for surgical tasks remains under-explored compared
to recent advancements in solving household manipulation tasks. These successes
have been largely driven by (1) advanced models, such as transformers and
diffusion models, and (2) large-scale data utilization. Aiming to extend these
successes to the domain of surgical robotics, we propose a diffusion-based
policy learning framework, called Diffusion Stabilizer Policy (DSP), which
enables training with imperfect or even failed trajectories. Our approach
consists of two stages: first, we train the diffusion stabilizer policy using
only clean data. Then, the policy is continuously updated using a mixture of
clean and perturbed data, with filtering based on the prediction error on
actions. Comprehensive experiments conducted in various surgical environments
demonstrate the superior performance of our method in perturbation-free
settings and its robustness when handling perturbed demonstrations.
