  Wheeled robots have recently demonstrated superior mechanical capability to
traverse vertically challenging terrain (e.g., extremely rugged boulders
comparable in size to the vehicles themselves). Negotiating such terrain
introduces significant variations of vehicle pose in all six Degrees-of-Freedom
(DoFs), leading to imbalanced contact forces, varying momentum, and chassis
deformation due to non-rigid tires and suspensions. To autonomously navigate on
vertically challenging terrain, all these factors need to be efficiently
reasoned within limited onboard computation and strict real-time constraints.
In this paper, we propose a 6-DoF kinodynamics learning approach that is
attentive only to the specific underlying terrain critical to the current
vehicle-terrain interaction, so that it can be efficiently queried in real-time
motion planners onboard small robots. Physical experiment results show our
Terrain-Attentive Learning demonstrates on average 51.1% reduction in model
prediction error among all 6 DoFs compared to a state-of-the-art model for
vertically challenging terrain.
