  Recent advances in machine learning models allowed robots to identify objects
on a perceptual nonsymbolic level (e.g., through sensor fusion and natural
language understanding). However, these primarily black-box learning models
still lack interpretation and transferability and require high data and
computational demand. An alternative solution is to teach a robot on both
perceptual nonsymbolic and conceptual symbolic levels through hybrid
neurosymbolic learning approaches with expert feedback (i.e., human-in-the-loop
learning). This work proposes a concept for this user-centered hybrid learning
paradigm that focuses on robotic surgical situations. While most recent
research focused on hybrid learning for non-robotic and some generic robotic
domains, little work focuses on surgical robotics. We survey this related
research while focusing on human-in-the-loop surgical robotic systems. This
evaluation highlights the most prominent solutions for autonomous surgical
robots and the challenges surgeons face when interacting with these systems.
Finally, we envision possible ways to address these challenges using online
apprenticeship learning based on implicit and explicit feedback from expert
surgeons.
