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

TL;DR

This paper reviews user-centered hybrid neurosymbolic learning approaches for robotic surgical systems, emphasizing human-in-the-loop methods to improve interpretability and transferability in surgical robotics.

Contribution

It introduces a conceptual framework for hybrid neurosymbolic learning in surgical robotics and surveys existing research, highlighting challenges and future directions.

Findings

Most hybrid learning research focuses on non-surgical domains.

Surgical robotics face unique challenges in human interaction and system autonomy.

Online apprenticeship learning can address surgeon-system interaction challenges.

Abstract

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.