Limin Zhu — accepted AI-conference papers

2026

Hierarchical Terrain-Aware Navigation via Bayesian-Guided RRT* and Flat-Corridor CiLQR

Tianwei Niu, Shoukun Wang, Dongfang Li, Rob Law, Limin Zhu, Edmond Qi Wu

RA-L 2026

Autonomous navigation in unstructured, off-road environments remains a critical challenge due to complex terrain and limited prior knowledge. This letter presents a hierarchical framework to generate paths across locally flat regions and optimize spatio-temporal trajectories under vehicle kinodynami

Cited by 1 Source ↗Scholar ↗

2020

Learning Semantic Keypoint Representations for Door Opening Manipulation

Jiayu Wang, Shize Lin, Chuxiong Hu, Yu Zhu, Limin Zhu

RA-L 2020

We consider a scenario where a robot is capable of autonomously opening previously unseen doors. Prior works either use model-based methods that rely strongly on accurate kinematic models, or learn a policy from scratch through trial-and-error, which cannot generalize to large variations in shape, a

Cited by 18 Source ↗Scholar ↗