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7. Motion Synthesis and Control Learning for (Un)Knotting Deformable Linear Objects

7. Motion Synthesis and Control Learning for (Un)Knotting Deformable Linear Objects

This video was recorded at AAAI 2009: AI Video Competition. This research deals with motion planning and control for Deformable Linear Objects (DLOs). It is still a complex task to get a robot manipulate a rope or cloth. To realise this vision of getting a robot to handle dexterous objects, we have taken the simplest object i.e. a DLO for our study purpose. The operations performed on the DLO are knot-(un)tying. The DLO is thus parameterised as a Knot and we make the DLO (un)tied into various knot types. The mathematical branch of Knot Theory is extensively used here to realise the Knots. The configuration space that the Knot can move is computed by the Knot Energy. We use the Minimum Distance Knot energy here. With this, we create a hierarchical graph structure with nodes corresponding to optimal knot configurations obtained by optimising this Knot energy functional. Thus by navigating this graph, we are able to (un)tie various knots. The study looks into 3 simple and 2 complex knot types. Motion control while (un)tying is also brought about using the SARSA(λ) [Reinforcement Learning] algorithm. The motion planner is resilient to perturbations as well. Thus by devising Knot Energy together with SARSA(λ), we have built a multi-scale, reactive knot (un)tying motion planner. Results show that our method is incredibly faster than normal Probabilistic and feedback control methods. For more, please refer to my MSc thesis titled 'Multi-scale, Reactive Motion Planning with Deformable Linear Objects' at http://www.inf.ed.ac.uk/publications/thesis/online/IM080596.pdf or for the complete version including Motion control at http://www.mediafire.com/file/dozd2zm3mam/MSc thesis - SARSA and Knot energy version.pdf.

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