  We present an efficient algorithm for motion planning and control of a robot
system with a high number of degrees-of-freedom. These include high-DOF soft
robots or an articulated robot interacting with a deformable environment. Our
approach takes into account dynamics constraints and present a novel technique
to accelerate the forward dynamic computation using a data-driven method. We
precompute the forward dynamic function of the robot system on a hierarchical
adaptive grid. Furthermore, we exploit the properties of underactuated robot
systems and perform these computations for a few DOFs. We provide error bounds
for our approximate forward dynamics computation and use our approach for
optimization-based motion planning and reinforcement-learning-based feedback
control. Our formulation is used for motion planning of two high DOF robot
systems: a high-DOF line-actuated elastic robot arm and an underwater swimming
robot operating in water. As compared to prior techniques based on exact
dynamic function computation, we observe one to two orders of magnitude
improvement in performance.
