Generalization in Robotic Manipulation Through The Use of Non-Rigid Registration

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Combining self-supervised learning and imitation for vision-based rope manipulation (2017, methodology): “[26] used non-rigid registration [27] for transferring human demonstrated trajectories for rope manipulation.”
Sim-to-Real Reinforcement Learning for Deformable Object Manipulation (2018, background): “Key domains of interest are home assistance robotics (cloth folding [1], bed making [2], getting dressed [3, 4]); medicine (robot surgery [5], suturing [6]); and industry (cable insertion [7]).”
A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario (2013, methodology): “Our method for generating end-effector trajectories in this paper is based on the previous work of the authors in [12].”
Learning to Manipulate Deformable Objects without Demonstrations (2019, background): “Although this produces better behavior, transferring it to a robot involves solving the difficult state-estimation problem [51, 52].”