Learning generalizable behaviors from demonstration.

Generalizing prior experiences to complete new tasks is a challenging and unsolved problem in robotics. In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control (). The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors.

Phase transition of displacive type in PbZr(0.94)Ti(0.06)O(3).

In this paper the electrostrictive properties above T(εmax), represented by the field-related M and polarization-related Q coefficients, have been reported for Pb(Zr(1-x)Ti(x))O(3) ceramics with x = 0.03-0.10.