Yingying Wu | 巫莹莹

I am an incoming Ph.D. student at the School of Intelligence Science and Technology, Peking University, advised by Prof. Song-Chun Zhu. I received my B.Eng. degree from the Department of Automation at Tsinghua University in 2026. Currently, I am a research intern at the Beijing Institute for General Artificial Intelligence (BIGAI), advised by Dr. Tengyu Liu and Dr. Siyuan Huang.

My research interests include Robotics and Dexterous Manipulation.

Email  /  Google Scholar  /  Github

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News

  • [2026/09] 🎉 Two papers were accepted to CoRL 2026.
  • [2026/07] 🎉 One paper was accepted to the 4th RSS Workshop on Dexterous Manipulation: Scalable Learning for Human-Level Skills.
  • [2025/08] 🎉 ControlVLA gets accepted to CoRL 2025, see you in Seoul!

Publications
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TeleDexter: Towards Human-level Dexterous Teleoperation

Puhao Li*, Zeyuan Chen*, Yingying Wu*, Pengkun Wei, Yuyang Li, Tianyu Wang, Jiaxiao Shi, Mingrui Yu, Baoxiong Jia, Song-Chun Zhu, Tengyu Liu, Siyuan Huang
[CoRL 2026] Conference on Robot Learning 2026

We introduce TeleDexter, a hand-object co-tracking controller that maps operator intent into learned low-level contact execution for dexterous teleoperation. The system transfers zero-shot to real robots and enables challenging in-hand reorientation and long-horizon tool-use tasks.
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ArtManip: Category-Level Articulated In-Hand Manipulation

Yang Yang*, Tengyu Liu*, Puhao Li, Zeyuan Chen, Yuyang Li, Xingwan Wang, Yingying Wu, Zhaopeng Cui, Siyuan Huang
[CoRL 2026] Conference on Robot Learning 2026

We introduce the first category-level articulated in-hand manipulation method that generalizes across object instances and diverse initial grasps. The system combines automated articulated-object and functional-grasp synthesis with robust two-stage policy learning, enabling zero-shot transfer to real objects with varied shapes and joint mechanics.
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained VLA models

Puhao Li, Yingying Wu, Ziheng Xi, Wanlin Li, Yuzhe Huang, Zhiyuan Zhang, Yinghan Chen, Jianan Wang, Song-Chun Zhu, Tengyu Liu, Siyuan Huang
[CoRL 2025]

We introduce ControlVLA, a few-shot object-centric adaptation method for pre-trained VLA. By reducing demonstrations requirements, ControlVLA lowers barriers to deploying robots in diverse scenarios.








Borrowed from Jon Barron.