  This paper presents a learning-based approach to consider the effect of
unobservable world states in kinodynamic motion planning in order to enable
accurate high-speed off-road navigation on unstructured terrain. Existing
kinodynamic motion planners either operate in structured and homogeneous
environments and thus do not need to explicitly account for terrain-vehicle
interaction, or assume a set of discrete terrain classes. However, when
operating on unstructured terrain, especially at high speeds, even small
variations in the environment will be magnified and cause inaccurate plan
execution. In this paper, to capture the complex kinodynamic model and
mathematically unknown world state, we learn a kinodynamic planner in a
data-driven manner with onboard inertial observations. Our approach is tested
on a physical robot in different indoor and outdoor environments, enables fast
and accurate off-road navigation, and outperforms environment-independent
alternatives, demonstrating 52.4% to 86.9% improvement in terms of plan
execution success rate while traveling at high speeds.
