  Control of off-road vehicles is challenging due to the complex dynamic
interactions with the terrain. Accurate modeling of these interactions is
important to optimize driving performance, but the relevant physical phenomena
are too complex to model from first principles. Therefore, we present an
offline meta-learning algorithm to construct a rapidly-tunable model of
residual dynamics and disturbances. Our model processes terrain images into
features using a visual foundation model (VFM), then maps these features and
the vehicle state to an estimate of the current actuation matrix using a deep
neural network (DNN). We then combine this model with composite adaptive
control to modify the last layer of the DNN in real time, accounting for the
remaining terrain interactions not captured during offline training. We provide
mathematical guarantees of stability and robustness for our controller and
demonstrate the effectiveness of our method through simulations and hardware
experiments with a tracked vehicle and a car-like robot. We evaluate our method
outdoors on different slopes with varying slippage and actuator degradation
disturbances, and compare against an adaptive controller that does not use the
VFM terrain features. We show significant improvement over the baseline in both
hardware experimentation and simulation.
