| Learning Fast Grasping Strategies using Multiple Attractors based Dynamical Systems Christophe Paccolat (MT) This project will deal with learning of fast and coordinated hand-arm grasping schemes under the Programming by Demonstration framework. The planning and control of the complete hand-arm system is extremely non-linear and complex due to its high dimensionality. Moreover, in the event of a sudden perturbation, the controller needs to update the motion of both hand and arm in order to successfully form the grasp around the new target. The student will use techniques based on Coupled Dynamical Systems with single and multiple attractors in order to achieve fast and reliable responses. The whole project will comprise of implementing the current state of the controller on the 7-dof WAM arm with 4-dof Barrett Hand (30%), learning multiple object affordances into a multi-attractor GMM (40%), developing a C++ library for multiple attractor GMR (30%). The detailed workflow would be as follows: 1) Exporting the current controller based on coupled dynamical systems from iCub to the WAM platform. This will require a study of the morphology of the two robots and mapping the learned model accordingly between the two. This will be followed by implementing an experiment involving fast perturbation. 2) Learning multiple affordances of a complex object into a single GMM with multiple attractors. A simple toy example developed in MATLAB would be developed as proof of concept. 3) Developing a C++ library for learning multiple attractor GMMs. Project: Master Project at EPFL Period: 21.02.2011 – 09.07.2011 Section(s): MT Type: Theory(40%), Robot Implementation (30%), Programming (30%) Knowledge(s): Dynamical Systems, MATLAB, C++ Subject(s): Imitation Learning, Dynamical Systems, Gaussian Mixture Regression Responsible(s): Ashwini Shukla, Aude Billard |
