3 accepted papers
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
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
The ability to perform reliable long-horizon task planning is crucial for deploying robots in real-world environments. However, directly employing Large Language Models (LLMs) as action sequence generators often results in low success rates due to their limited reasoning ability for long-horizon emb