Motion generation of robotic surgical tasks: Learning from expert demonstrations

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On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, methodology): “, knottying, suturing, or stitching) to reduce the cognitive workload on the surgical team [16], [17].”
Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning (2017, methodology): “[26] proposed a demonstration-based framework that used Gaussian Mixture Models (GMMs) for motion generation.”
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards (2018, background): “2010), and demonstrator skill-assessment (Reiley et al. 2010; Gao et al. 2014).”
Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions (2016, background): “MLapproaches have the potential to learn a statistical model of surgical skills of experienced surgeons from data collected in the OR [122].”
JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling (2014, methodology): “Other research conducted using kinematic data includes applying Gaussian mixture regression to reproduce expert motion trajectories as described in [13].”