Deep Movement Primitives: toward Breast Cancer Examination Robot

Sanni, Oluwatoin, Bonvicini, Giorgio, Khan, Muhammad Arshad , Lo ́pez-Custodio, Pablo C., Nazari, Kiyanoush and Ghalamzan Esfahani, Amir (2021) Deep Movement Primitives: toward Breast Cancer Examination Robot. In: AAAI Conference on Artificial Intelligence 2022.

Deep Movement Primitives: toward Breast Cancer Examination Robot
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Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldwide. However, robot programming for breast palpating with different geometries is very complex and unsolved. Robot learning from demonstrations (LfD) re- duces the programming time and cost. However, the available LfD are lacking the modelling of the manipulation path/trajectory as an explicit function of the visual sensory information. This paper presents a novel approach to manipulation path/trajectory planning called deep Movement Primitives that successfully generates the movements of a manipulator to reach a breast phantom and perform the palpation. We show the effectiveness of our approach by a series of real-robot experiments of reaching and palpating a breast phantom. The experimental results indicate our approach outperforms the state-of-the-art method.

Keywords:Deep learning, Robot path planing, Breast Cancer Examination
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:47605
Deposited On:28 Feb 2022 09:34

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