Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning

• URL: http://arxiv.org/abs/2409.15688v1
• Date: Tue, 24 Sep 2024 03:01:30 GMT
• Title: Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning
• Authors: Min Tan, Yushun Tao, Boyun Zheng, GaoSheng Xie, Lijuan Feng, Zeyang Xia, Jing Xiong,
• Abstract summary: We propose a Human Intervention (HI)-based Proximal Policy Optimization framework, dubbed HI-PPO, to enhance RDE's safety. We introduce an Enhanced Exploration Mechanism (EEM) to address the low exploration efficiency of the standard PPO. We also introduce a reward-penalty adjustment (RPA) to penalize unsafe actions during initial interventions.
• Score: 5.520042381826271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing application of automated robotic digestive endoscopy (RDE), ensuring safe and efficient navigation in the unstructured and narrow digestive tract has become a critical challenge. Existing automated reinforcement learning navigation algorithms, often result in potentially risky collisions due to the absence of essential human intervention, which significantly limits the safety and effectiveness of RDE in actual clinical practice. To address this limitation, we proposed a Human Intervention (HI)-based Proximal Policy Optimization (PPO) framework, dubbed HI-PPO, which incorporates expert knowledge to enhance RDE's safety. Specifically, we introduce an Enhanced Exploration Mechanism (EEM) to address the low exploration efficiency of the standard PPO. Additionally, a reward-penalty adjustment (RPA) is implemented to penalize unsafe actions during initial interventions. Furthermore, Behavior Cloning Similarity (BCS) is included as an auxiliary objective to ensure the agent emulates expert actions. Comparative experiments conducted in a simulated platform across various anatomical colon segments demonstrate that our model effectively and safely guides RDE.

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