  We present a novel motion planning algorithm for transferring a liquid body
from a source to a target container. Our approach uses a receding-horizon
optimization strategy that takes into account fluid constraints and avoids
collisions. In order to efficiently handle the high-dimensional configuration
space of a liquid body, we use system identification to learn its dynamics
characteristics using a neural network. We generate the training dataset using
stochastic optimization in a transfer-problem-specific search space. The
runtime feedback motion planner is used for real-time planning and we observe
high success rate in our simulated 2D and 3D fluid transfer benchmarks.
