Real-time Digital Double Framework to Predict Collapsible Terrains for Legged Robots

TL;DR

This paper introduces a real-time digital double framework that uses simulation and real robot discrepancy analysis to improve terrain perception, especially for soft, collapsible terrains, enhancing legged robot navigation.

Contribution

It presents a novel digital double approach that estimates terrain collapsibility through discrepancy analysis, enabling real-time perception improvements for legged robots.

Findings

Successfully estimates terrain collapsibility in real-time

Generalizes well from simulation to real-world scenarios

Enhances detection of nuanced ground conditions

Abstract

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.