Helical Control in Latent Space: Enhancing Robotic Craniotomy Precision in Uncertain Environments

Abstract

In this paper, we introduce a double-stage transfer learning framework based on expert data. It employs probabilistic graphical models to effectively capture helical periodic features in the latent space, integrating Bayesian variational inference and neural networks for implementation. Compared to traditional methods, it achieves high precision and stable control even in environments with limited observation signals and high noise levels. We have successfully applied this method to a biomedical task of a simulated cranial window procedure. Preliminary results show promising performance comparable to those of human experts with only image information, further validating the efficacy of the proposed method.