Anqi Li | 李安齐

Hi, I am a third-year undergraduate student at Yuanpei College, Peking University, majoring in Artificial Intelligence.
I'm currently working in the EPIC Lab under the supervision of Prof. He Wang and working closely with Jiazhao Zhang. I focus on embodied AI, particularly Vision-Language-Action (VLA) models and their applications in robotic navigation.
Outside of research, I enjoy playing basketball, performing on the trombone, and speedcubing.

Research

	UrbanVLA: A Vision-Language-Action Model for Urban Micromobility Anqi Li, Zhiyong Wang, Jiazhao Zhang, Minghan Li, Yunpeng Qi, Zhibo Chen, Zhizheng Zhang^†, He Wang^† arXiv preprint* Paper Project page UrbanVLA is a route-conditioned Vision-Language-Action model for urban micromobility. It aligns noisy navigation-tool routes with visual observations to enable scalable, long-horizon navigation. Trained via a two stage pipeline including SFT and RFT, UrbanVLA outperforms baselines by over 55% on MetaUrban and achieves robust real-world navigation across 500m+ routes.
	Embodied Navigation Foundation Model Jiazhao Zhang, Anqi Li, Yunpeng Qi, Minghan Li, Jiahang Liu, Shaoan Wang, Haoran Liu, Gengze Zhou, Yuze Wu, Xingxing Li, Yuxin Fan, Wenjun Li, Zhibo Chen, Fei Gao, Qi Wu, Zhizheng Zhang^†, He Wang^† arXiv preprint Paper Project page NavFoM is a cross-embodiment and cross-task navigation model trained on 8 million samples encompassing quadrupeds, drones, wheeled robots, and vehicles, spanning tasks including vision-and-language navigation, object searching, target tracking, and autonomous driving.
	TrackVLA: Embodied Visual Tracking in the Wild Shaoan Wang, Jiazhao Zhang, Minghan Li, Jiahang Liu, Anqi Li, Kui Wu, Fangwei Zhong, Junzhi Yu, Zhizheng Zhang^†, He Wang^† CoRL 2025 Paper Code Project page TrackVLA is a vision-language-action model capable of simultaneous object recognition and visual tracking, trained on a dataset of 1.7 million samples. It demonstrates robust tracking, long-horizon tracking, and cross-domain generalization across diverse challenging environments.