  Automating robotic surgery via learning from demonstration (LfD) techniques
is extremely challenging. This is because surgical tasks often involve
sequential decision-making processes with complex interactions of physical
objects and have low tolerance for mistakes. Prior works assume that all
demonstrations are fully observable and optimal, which might not be practical
in the real world. This paper introduces a sample-efficient method that learns
a robust reward function from a limited amount of ranked suboptimal
demonstrations consisting of partial-view point cloud observations. The method
then learns a policy by optimizing the learned reward function using
reinforcement learning (RL). We show that using a learned reward function to
obtain a policy is more robust than pure imitation learning. We apply our
approach on a physical surgical electrocautery task and demonstrate that our
method can perform well even when the provided demonstrations are suboptimal
and the observations are high-dimensional point clouds. Code and videos
available here: https://sites.google.com/view/lfdinelectrocautery
