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A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was originally designed. In this work, we show how a challenging minigolf-like task can be efficiently learned by the robot using a basic hitting motion model and a task-specific adaptation of the hitting parameters: hitting speed and hitting angle. We propose an approach to learn the hitting parameters for a minigolf field using a set of provided examples. This is a non- trivial problem since the successful choice of hitting parameters generally represent a highly non-linear, multi-valued map from the situation-representation to the hitting parameters. We show that by limiting the problem to learning one combination of hitting parameters for each input, a high-performance model of the hitting parameters can be learned using only a small set of training data. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring hitting parameters for the minigolf task. We validate our approach on the 7 degrees of freedom Barrett WAM robotic arm in both a simulated and real environment.
In this thesis we tackle the problem of goal-oriented adaptation of a robot hitting motion. We propose the parameters that must be learned in order to use and adapt a basic hitting motion to play minigolf. Then, two different statistical methods are used to learn these parameters. The two methods are evaluated and compared. To validate the proposed approach, a minigolf control module is developed for a robotic arm. Using the different learning techniques, we show that a robot can learn the non-trivial task of deciding how the ball should be hit for a given position on a minigolf field. The result is a robust minigolf-playing system that outperforms most human players using only a small set of training examples.
A current trend in robotics is to define robot motions so that they can be easily adopted to situations beyond those for which the motion was originally designed. In this work, we show how the challenging task of playing minigolf can be efficiently tackled by first learning a basic hitting motion model, and then learning to adapt it to different situations. We model the basic hitting motion with an autonomous dynamical systems, and solve the problem of learning the parameters of the model from a set of demonstrations through a constrained optimization. To hit the ball with the appropriate hitting angle and speed, a nonlinear model of the hitting parameters is estimated based on a set of examples of good hitting parameters. We compare two statistical methods, Gaussian Process Regression and Gaussian Mixture Regression in the context of inferring the hitting parameters for the minigolf task. We demonstrate the generalization ability of the model in various situations. We validate our approach on the 7 Degrees of Freedom (DoF) Barrett WAM arm and 6-DoF Katana arm in both simulated and real environments. © 2012 Copyright Taylor & Francis and The Robotics Society of Japan.