Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/244779
DC Field | Value | |
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dc.title | CONSTRAINED DEEP REINFORCEMENT LEARNING FOR ROBOT-ASSISTED SURGICAL TRAINING WITH SOFT TISSUE DEFORMATION | |
dc.contributor.author | ZENG WEI | |
dc.date.accessioned | 2023-08-31T18:00:37Z | |
dc.date.available | 2023-08-31T18:00:37Z | |
dc.date.issued | 2023-05-17 | |
dc.identifier.citation | ZENG WEI (2023-05-17). CONSTRAINED DEEP REINFORCEMENT LEARNING FOR ROBOT-ASSISTED SURGICAL TRAINING WITH SOFT TISSUE DEFORMATION. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/244779 | |
dc.description.abstract | In this thesis, we address the challenges in laparoscopic surgery training by leveraging Deep Reinforcement Learning (DRL). We develop a simulation environment using the SOFA framework and SofaPython3 plugin, allowing seamless integration with Python-based machine learning algorithms. Our Gym environment interfaces with standard RL algorithms for training and evaluation in a simulated surgical environment. We present an uncertainty-aware Model-Based Greedy Search (MBGS) algorithm for constrained RL, promoting safety during DRL training. The laparoscopic surgery task is specified as a target-reaching task, guiding the robot to move the end effector to a given target point in/around the liver while stabilizing environment parameters. The ensemble of uncertainty-aware networks predicts parameters, evaluating multiple actions to select the optimal one. Experimental results show effective parameter stabilization and rapid learning. The algorithm's efficiency and compatibility with other off-policy algorithms make it a promising tool for high-safety scenarios. | |
dc.language.iso | en | |
dc.subject | Laparoscopic surgery training, constrained deep reinforcement learning, soft-tissue simulation, SOFA framework, model-based greedy search | |
dc.type | Thesis | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.contributor.supervisor | Chee Kong Chui | |
dc.description.degree | Master's | |
dc.description.degreeconferred | MASTER OF ENGINEERING (CDE) | |
dc.identifier.orcid | 0000-0002-0953-5314 | |
Appears in Collections: | Master's Theses (Restricted) |
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