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We introduce an explainable, physics-aware, and end-to-end differentiablemodel which predicts the outcome of robot-terrain interaction from cameraimages. The proposed MonoForce model consists of a black-box module, whichpredicts robot-terrain interaction forces from the onboard camera, followed bya white-box module, which transforms these forces through the laws of classicalmechanics into the predicted trajectories. As the white-box model isimplemented as a differentiable ODE solver, it enables measuring the physicalconsistency between predicted forces and ground-truth trajectories of therobot. Consequently, it creates a self-supervised loss similar to MonoDepth. Tofacilitate the reproducibility of the paper, we provide the source code. Seethe project github for codes and supplementary materials such as videos anddata sequences.

MonoForce: Self-supervised learning of physics-aware grey-box model for predicting the robot-terrain interaction