Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when t...

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Bibliographic Details
Main Authors: Hase, Hannes, Azampour, Mohammad Farid, Tirindelli, Maria, Paschali, Magdalini, Simson, Walter, Fatemizadeh, Emad, Navab, Nassir
Format: Conference Proceeding
Language:English
Subjects:
Intelligent robots
Navigation
Reinforcement learning
Supervised learning
Task analysis
Testing
Ultrasonic imaging
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Summary:In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when to terminate the task.Our method is trained and evaluated on an in-house collected data-set of 34 volunteers and when compared to pure RL and supervised learning (SL) techniques, it performs substantially better, which highlights the suitability of RL navigation for US-guided procedures. When testing our proposed model, we obtained a 82.91% chance of navigating correctly to the sacrum from 165 different starting positions on 5 different unseen simulated environments.
ISSN:2153-0866
DOI:10.1109/IROS45743.2020.9340913