Yubiao Ma — accepted AI-conference papers

2026

TerAdapt: Proprioceptive Terrain-Adaptive Locomotion via Codebook Aligned Representation Learning

Yubiao Ma, Han Yu, Kai Guo, Chongming Chen, Wuwei Huang, Boyang Xing +2

RA-L 2026

Humanoid robots aim to achieve human-like locomotion in unstructured environments. However, designing a controller for such robots is highly challenging due to their inherent instability and the requirement to adapt to diverse terrains. To address this problem, we present TerAdapt, a proprioceptive

Cited by 0 Source ↗Scholar ↗

2026

VPIES: Variational Privileged Information Encoder as Scaffold for Legged Locomotion Learning

Yubiao Ma, Han Yu, Kai Guo, Boyang Xing, Xuemei Ren, Dongdong Zheng

RA-L 2026

Legged robots face significant challenges in complex terrains due to partial observability. While teacher-student frameworks address this through imitation, they often cause representation mismatch and covariate shift, limiting deployment robustness. To address these limitations, we propose the Vari

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