Paper Info
Title
Real-time Digital Double Framework to Predict Collapsible Terrains for Legged Robots.
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.
Year
DOI
Venue
2022
10.1109/IROS47612.2022.9981613
IEEE/RJS International Conference on Intelligent RObots and Systems (IROS)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Garen Haddeler100.34
Hari P. Palanivelu200.34
Yung Chuen Ng300.34
Fabien Colonnier482.15
Albertus H. Adiwahono500.68
Zhibin Li6416.93
Chee-Meng Chew737540.58
Meng Yee800.34
Chuah900.34