Synthesizing goal-directed actions from a library of example movements
Aleš Ude, Marcia Riley, Bojan Nemec, Andrej Kos, Tamim Asfour, Gordon Cheng · 2007
We present a new learning framework for synthesizing goal-directed actions from example movements. The approach is based on the memorization of training data and locally weighted regression to compute suitable movements for a large range of situations. The proposed method avoids making specific assumptions about an adequate representation of the task. Instead, we use a general representation based on fifth order splines. The data used for learning comes either from the observation of events in the Cartesian space or from the actual movement execution on the robot. Thus it informs us about the appropriate motion in the example situations. We show that by applying locally weighted regression to such data, we can generate actions having proper dynamics to solve the given task. To test the validity of the approach, we present simulation results under various conditions as well as experiments on a real robot.