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Robot-assisted intervention has shown reduced radiation exposure tophysicians and improved precision in clinical trials. However, existingvascular robotic systems follow master-slave control mode and entirely rely onmanual commands. This paper proposes a novel offline reinforcement learningalgorithm, Conservative Actor-critic with SmOoth Gradient (CASOG), to learnmanipulation skills from human demonstrations on vascular robotic systems. Theproposed algorithm conservatively estimates Q-function and smooths gradients ofconvolution layers to deal with distribution shift and overfitting issues.Furthermore, to focus on complex manipulations, transitions with largertemporal-difference error are sampled with higher probability. Comparativeexperiments in a pre-clinical environment demonstrate that CASOG can deliverguidewire to the target at a success rate of 94.00\% and mean backward steps of14.07, performing closer to humans and better than prior offline reinforcementlearning methods. These results indicate that the proposed algorithm ispromising to improve the autonomy of vascular robotic systems.

CASOG: Conservative Actor-critic with SmOoth Gradient for Skill Learning in Robot-Assisted Intervention