Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Oct 10, 2013
We target the difficult problem of catching in-flight objects with uneven shapes. This requires the solution of three complex problems: predicting accurately the trajectory of fastmoving objects, predicting the feasible catching configuration and planning the arm motion, all within milliseconds. We follow a programming-by-demonstration approach in order to learn models of the object and arm dynamics from throwing examples. We propose a new methodology for finding a feasible catching configuration in a probabilistic manner. We leverage the strength of dynamical systems for encoding motion from several demonstrations. This enables fast and on-line adaptation of the arm motion in the presence of sensor uncertainty. We validate the approach in simulation with the iCub humanoid robot and in real-world experiment with the KUKA LWR 4+ (7 degrees of freedom arm robot) for catching a hammer, a tennis racket, a empty bottle, a partially filled bottle and a cardboard box.