Deep Movement Primitives: Toward Breast Cancer Examination Robot


  • Oluwatoyin Sanni University of Lincoln
  • Giorgio Bonvicini Politecnico di Milano
  • Muhammad Arshad Khan University of Lincoln
  • Pablo C. López-Custodio University of Lincoln
  • Kiyanoush Nazari Univercity of Lincoln
  • Amir M. Ghalamzan E. University of Lincoln



Intelligent Robotics (ROB)


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) reduces 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.




How to Cite

Sanni, O., Bonvicini, G., Khan, M. A., López-Custodio, P. C., Nazari, K., & Ghalamzan E., A. M. (2022). Deep Movement Primitives: Toward Breast Cancer Examination Robot. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12126-12134.