Knotting/Unknotting Manipulation of Deformable Linear Objects

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Combining self-supervised learning and imitation for vision-based rope manipulation (2017, background): “[5] chose a sequence of robotic hand motion primitives from rope cross states inferred by a planner to achieve a desired knot from a given initial state of the rope.”
Learning to Manipulate Deformable Objects without Demonstrations (2019, background): “Early work [49, 66, 39] focused on using planning for linearly deformable objects like ropes.”
Learning from Demonstrations Through the Use of Non-rigid Registration (2013, background): “Researchers have addressed the problemwithmotion planning [12, 13], learning from observation (using knot theoretic representations) [14], robotic hands with tactile feedback [15], and fixtures that enable robust open-loop”
Flexible Object Manipulation (2010, background): “Wakamatsu, Arai and Hirai developed a very detailed description of the theory involved in tying and untying knots with robots [82].”
Manipulation Planning for Deformable Linear Objects (2007, methodology): “Similar ideas are being used in [30] to plan knotting and unknotting of DLOs, and further developed for tightening of the knots.”
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data (2020, background): “manipulation [18, 25, 27, 40], but these techniques can fail to generalize to novel configurations.”