  We present a Learning from Demonstration method for teaching robots to
perform search strategies imitated from humans in scenarios where alignment
tasks fail due to position uncertainty. The method utilizes human
demonstrations to learn both a state invariant dynamics model and an
exploration distribution that captures the search area covered by the
demonstrator. We present two alternative algorithms for computing a search
trajectory from the exploration distribution, one based on sampling and another
based on deterministic ergodic control. We augment the search trajectory with
forces learnt through the dynamics model to enable searching both in force and
position domains. An impedance controller with superposed forces is used for
reproducing the learnt strategy. We experimentally evaluate the method on a
KUKA LWR4+ performing a 2D peg-in-hole and a 3D electricity socket task.
Results show that the proposed method can, with only few human demonstrations,
learn to complete the search task.
