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Recent advances in machine learning models allowed robots to identify objectson a perceptual nonsymbolic level (e.g., through sensor fusion and naturallanguage understanding). However, these primarily black-box learning modelsstill lack interpretation and transferability and require high data andcomputational demand. An alternative solution is to teach a robot on bothperceptual nonsymbolic and conceptual symbolic levels through hybridneurosymbolic learning approaches with expert feedback (i.e., human-in-the-looplearning). This work proposes a concept for this user-centered hybrid learningparadigm that focuses on robotic surgical situations. While most recentresearch focused on hybrid learning for non-robotic and some generic roboticdomains, little work focuses on surgical robotics. We survey this relatedresearch while focusing on human-in-the-loop surgical robotic systems. Thisevaluation highlights the most prominent solutions for autonomous surgicalrobots and the challenges surgeons face when interacting with these systems.Finally, we envision possible ways to address these challenges using onlineapprenticeship learning based on implicit and explicit feedback from expertsurgeons.

Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems