Lingqi Zeng — accepted AI-conference papers

2026

Scalable Vision-Language-Action Model Pretraining for Robotic Dexterous Manipulation with Real-Life Human Activity Videos

Qixiu Li, Yu Deng, Yaobo Liang, Lin Luo, Lei Zhou, Chengtang Yao +11

ICRA 2026poster

This paper presents an approach for pretraining robotic manipulation Vision-Language-Action (VLA) models using a large corpus of unscripted real-life video recordings of human hand activities. Treating human hand as dexterous robot end-effector, we show that "in-the-wild" egocentric human videos wit…

Cited by 0 Scholar ↗

2025

Learning Adaptive Dexterous Grasping from Single Demonstrations

Liangzhi Shi, Yulin Liu, Lingqi Zeng, Bo Ai, Zhengdong Hong, Hao Su

IROS 2025

How can robots learn dexterous grasping skills efficiently and apply them adaptively based on user instructions? This work tackles two key challenges: efficient skill acquisition from limited human demonstrations and context-driven skill selection. We introduce AdaDexGrasp, a framework that learns a

Cited by 4 Source ↗Scholar ↗