Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study
arxiv(2024)
摘要
In this work, we develop an open-source surgical simulation environment that
includes a realistic model obtained by MRI-scanning a physical phantom, for the
purpose of training and evaluating a Learning from Demonstration (LfD)
algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement
Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the
needle trajectory, rather than the instruments, to obtain better generality
with respect to needle grasps. We conduct a user study to collect multiple
suturing demonstrations and perform a comprehensive analysis of the ability of
the LfD algorithm to generalize from a demonstration at one location in one
phantom to different locations in the same phantom and to a different phantom.
Our results indicate good generalization, on the order of 91.5
from more experienced subjects, indicating the need to integrate skill
assessment in the future.
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