  Inspired by the digital twinning systems, a novel real-time digital double
framework is developed to enhance robot perception of the terrain conditions.
Based on the very same physical model and motion control, this work exploits
the use of such simulated digital double synchronized with a real robot to
capture and extract discrepancy information between the two systems, which
provides high dimensional cues in multiple physical quantities to represent
differences between the modelled and the real world. Soft, non-rigid terrains
cause common failures in legged locomotion, whereby visual perception solely is
insufficient in estimating such physical properties of terrains. We used
digital double to develop the estimation of the collapsibility, which addressed
this issue through physical interactions during dynamic walking. The
discrepancy in sensory measurements between the real robot and its digital
double are used as input of a learning-based algorithm for terrain
collapsibility analysis. Although trained only in simulation, the learned model
can perform collapsibility estimation successfully in both simulation and real
world. Our evaluation of results showed the generalization to different
scenarios and the advantages of the digital double to reliably detect nuances
in ground conditions.
