A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario

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Combining self-supervised learning and imitation for vision-based rope manipulation (2017, background): “Prior works have considered problems such as surgical suturing [8], [9], towel folding [7], knot tying and rope manipulation among many others.”
A guide to deep learning in healthcare (2019, background): “Modern genomic technologies collect a wide variety of measurements, from an individual’s DNA sequence to the quantity of”
Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning (2017, methodology): “[26] all approached manipulation of suture material using Learning From Demonstrations (LfD), an approach that uses demonstration trajectories to learn how to execute specific tasks.”
Modeling, learning, perception, and control methods for deformable object manipulation (2021, methodology): “Ropes (13, 14, 15) Fabrics (16, 17) Simple to implement Lacking physical interpretability Sponges (17, 18) Rubber spheres (18 where k j denotes the stiffness of the j th constraint C j ( x 1 , x 2 , …, x n ) = 0, j = 1,2, ….”
Learning to Manipulate Deformable Objects without Demonstrations (2019, background): “These expert demonstrations can also be used to learn wire threading [47], [48].”
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data (2020, unknown): “videos such as [8, 10, 29, 32, 34, 35].”