  Accurate control of robots at high speeds requires a control system that can
take into account the kinodynamic interactions of the robot with the
environment. Prior works on learning inverse kinodynamic (IKD) models of robots
have shown success in capturing the complex kinodynamic effects. However, the
types of control problems these approaches can be applied to are limited only
to that of following pre-computed kinodynamically feasible trajectories. In
this paper we present Optim-FKD, a new formulation for accurate, high-speed
robot control that makes use of a learned forward kinodynamic (FKD) model and
non-linear least squares optimization. Optim-FKD can be used for accurate, high
speed control on any control task specifiable by a non-linear least squares
objective. Optim-FKD can solve for control objectives such as path following
and time-optimal control in real time, without needing access to pre-computed
kinodynamically feasible trajectories. We empirically demonstrate these
abilities of our approach through experiments on a scale one-tenth autonomous
car. Our results show that Optim-FKD can follow desired trajectories more
accurately and can find better solutions to optimal control problems than
baseline approaches.
