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.

翻译：近期机器学习模型的进展使机器人能够在感知非符号层面识别物体（例如通过传感器融合和自然语言理解）。然而，这些主要采用黑箱模式的学习模型仍缺乏可解释性与可迁移性，且需要大量数据和计算资源。一种替代方案是通过混合神经符号学习方法，结合专家反馈（即人在回路学习），在感知非符号与概念符号两个层面教导机器人。本文提出了一种聚焦于机器人手术场景的以用户为中心的混合学习范式概念。尽管当前研究多集中于非机器人领域及部分通用机器人领域的混合学习，但针对手术机器人的相关工作较少。我们围绕人在回路的手术机器人系统，对其相关研究进行了系统梳理。该评估揭示了自主手术机器人最具潜力的解决方案，以及外科医生与这些系统交互时面临的挑战。最后，我们展望了基于专家外科医生隐式与显式反馈的在线学徒学习，以应对这些挑战的可能路径。