We introduce an explainable, physics-aware, and end-to-end differentiable
model which predicts the outcome of robot-terrain interaction from camera
images. The proposed MonoForce model consists of a black-box module, which
predicts robot-terrain interaction forces from the onboard camera, followed by
a white-box module, which transforms these forces through the laws of classical
mechanics into the predicted trajectories. As the white-box model is
implemented as a differentiable ODE solver, it enables measuring the physical
consistency between predicted forces and ground-truth trajectories of the
robot. Consequently, it creates a self-supervised loss similar to MonoDepth. To
facilitate the reproducibility of the paper, we provide the source code. See
the project github for codes and supplementary materials such as videos and
data sequences