Learning to Play Mini-Golf from Human Demonstration using Autonomous Dynamical Systems

We present a new formulation for autonomous (i.e. time-independent) Dynamical Systems (DS) to perform discrete robot motions with em non-zero velocity at a given target. The proposed model ensures the convergence of all trajectories to the target, and is inherently robust to perturbations. We evaluated the performance of our proposed method to control right-handed swings in mini-golf.

[1]  Stefan Schaal,et al.  Learning and generalization of motor skills by learning from demonstration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[2]  A. Billard,et al.  Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models , 2011, IEEE Transactions on Robotics.

[3]  Aude Billard,et al.  Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models , 2011, IEEE Transactions on Robotics.

[4]  Aude Billard,et al.  Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Christoph H. Lampert,et al.  Movement templates for learning of hitting and batting , 2010, 2010 IEEE International Conference on Robotics and Automation.