  Ground robots require the crucial capability of traversing unstructured and
unprepared terrains and avoiding obstacles to complete tasks in real-world
robotics applications such as disaster response. When a robot operates in
off-road field environments such as forests, the robot's actual behaviors often
do not match its expected or planned behaviors, due to changes in the
characteristics of terrains and the robot itself. Therefore, the capability of
robot adaptation for consistent behavior generation is essential for
maneuverability on unstructured off-road terrains. In order to address the
challenge, we propose a novel method of self-reflective terrain-aware
adaptation for ground robots to generate consistent controls to navigate over
unstructured off-road terrains, which enables robots to more accurately execute
the expected behaviors through robot self-reflection while adapting to varying
unstructured terrains. To evaluate our method's performance, we conduct
extensive experiments using real ground robots with various functionality
changes over diverse unstructured off-road terrains. The comprehensive
experimental results have shown that our self-reflective terrain-aware
adaptation method enables ground robots to generate consistent navigational
behaviors and outperforms the compared previous and baseline techniques.
