Robotics: Science and Systems XXII

Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion

Tianyang Wu, Hanwei Guo, Yuhang Wang, Junshu Yang, Xinyang Sui, Jiayi Xie, Xingyu Chen, Zeyang Liu, Xuguang Lan

Abstract:

Reinforcement learning has shown strong promise for quadrupedal agile locomotion, even with proprioception-only sensing. In practice, however, sim-to-real gap and reward overfitting in complex terrains can produce policies that fail to transfer, while physical validation remains risky and inefficient. To address these challenges, we introduce a unified framework encompassing a Mixture-of-Experts (MoE) locomotion policy for robust multi-terrain representation with RoboGauge, a predictive assessment suite that quantifies sim-to-real transferability. The MoE policy employs a gated set of specialist experts to decompose latent terrain and command modeling, achieving superior deployment robustness and generalization via proprioception alone. RoboGauge further provides multi-dimensional proprioception-based metrics via sim-to-sim tests over terrains, difficulty levels, and domain randomizations, enabling reliable MoE policy selection without extensive physical trials. Experiments on a Unitree Go2 demonstrate robust locomotion on unseen challenging terrains, including snow, sand, stairs, slopes, and 30 cm obstacles. In dedicated high-speed tests, the robot reaches 4 m/s and exhibits an emergent narrow-width gait associated with improved stability at high velocity.

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Bibtex:

  
@INPROCEEDINGS{WuT-RSS-26, 
    AUTHOR    = {Tianyang Wu AND Hanwei Guo AND Yuhang Wang AND Junshu Yang AND Xinyang Sui AND Jiayi Xie AND Xingyu Chen AND Zeyang Liu AND Xuguang Lan}, 
    TITLE     = {{Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.156} 
}