  Terrain adaptation is an essential capability for a ground robot to
effectively traverse unstructured off-road terrain in real-world field
environments such as forests. However, the expected robot behaviors generated
by terrain adaptation methods cannot always be executed accurately due to
setbacks such as wheel slip and reduced tire pressure. To address this problem,
we propose a novel approach for consistent behavior generation that enables the
ground robot's actual behaviors to more accurately match expected behaviors
while adapting to a variety of unstructured off-road terrain. Our approach
learns offset behaviors that are used to compensate for the inconsistency
between the actual and expected behaviors without requiring the explicit
modeling of various setbacks. Our approach is also able to estimate the
importance of the multi-modal features to improve terrain representations for
better adaptation. In addition, we develop an algorithmic solver for our
formulated regularized optimization problem, which is guaranteed to converge to
the global optimal solution. To evaluate the method, we perform extensive
experiments using various unstructured off-road terrain in real-world field
environments. Experimental results have validated that our approach enables
robots to traverse complex unstructured off-road terrain with more navigational
behavior consistency, and it outperforms previous methods, particularly so on
challenging terrain.
