SurgIRL: Toward Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning

Abstract

Surgical automation holds immense potential to improve the outcome and accessibility of surgery. Recent studies use reinforcement learning to automate various surgical tasks. However, these policies are developed independently, and their reusability is limited when applied to other scenarios, making it more time-consuming for robots to incrementally solve tasks. Inspired by how human surgeons build their expertise, we propose Surgical Incremental Reinforcement Learning (SurgIRL). SurgIRL aims to (1) acquire new skills by referring to external policies (knowledge) and (2) build an expandable knowledge base and reuse it to solve multiple unseen tasks incrementally (incremental learning). Our SurgIRL framework includes three major components. We first define an expandable knowledge set containing heterogeneous policies that can be helpful for surgical tasks. Then, we propose Knowledge Inclusive Attention Network with mAximum Coverage Exploration (KIAN-ACE), which enhances learning performance through extensive navigation of the knowledge base. Finally, we develop incremental learning pipelines to expand and reuse a knowledge base and solve multiple surgical tasks sequentially. Our simulation experiments show that SurgIRL efficiently learns to automate ten surgical tasks separately or incrementally. We also demonstrate successful sim-to-real transfers of SurgIRL's policies on the da Vinci Research Kit (dVRK). The results represent an initial step towards lifelong robot learning for surgical automation.

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