Ruoqu Chen

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I am a first-year Ph.D. student at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, advised by Prof. Mengdi Xu. My research interests lie in dexterous manipulation and robot interaction. I received my bachelor’s degree from the Department of Automation at Tsinghua University, where I was fortunate to be advised by Prof. Xiang Li and Prof. Mingguo Zhao, and I joined the Stanford Vision and Learning Lab (SVL) as a summer research intern in 2025.

selected publications

Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction

Ruoqu Chen, Feixiang Ruan, Liu Cao, Zihao Wang, Botian Xu, Shiqin Tong, Jiajun Liu, Wanli Xing, Kaifeng Zhang, and Mengdi Xu

In RSS 2026 Workshop on Dexterous Manipulation(Spotlight) , 2026

Abs Website

A framework that learns visual-tactile dexterous manipulation skills from human videos by leveraging simulated interaction, bridging the gap between passive human demonstrations and executable robot policies.
UltraDP: Generalizable Carotid Ultrasound Scanning with Force-Aware Diffusion Policy

Ruoqu Chen, Xiangjie Yan, Kangchen Lv, Gao Huang, Zheng Li, and Xiang Li

In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025

Abs arXiv Website

Present UltraDP, a Diffusion-Policy-based method that receives multi-sensory inputs (ultrasound images, wrist camera image, contact wrench, and probe pose) and generates actions that are fit for multi-modal action distributions in autonomous ultrasound scanning. UltraDP achieves a 95% success rate in transverse scanning on previously unseen subjects, demonstrating its effectiveness.
HuMiT: Low-Latency Whole-Body Humanoid Teleoperation via Minimal Reference Tracking

Liu Cao, Botian Xu, Ruoqu Chen, Jiajun Liu, Mingzhi Pei, Zihao Wang, Zhongyu Li, and Mengdi Xu

Preprint (Under Review), 2026

Abs Website

HuMiT is a whole-body teleoperation system that drives a humanoid from sparse human keypoints alone, achieving 20 ms communication-to-inference latency to enable reactive whole-body skills such as ball catching and loco-manipulation.
Whole-Body Model Predictive Control for Mobile Manipulation with Task Priority Transition

Yushi Wang, Ruoqu Chen, and Mingguo Zhao

In 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025

Abs Website

Developed a Whole-Body Model Predictive Control (WBMPC) framework to manage task priorities and scheduling in multi-task mobile manipulation scenarios. Integrated task priorities into a unified weight matrix, enabling smooth transitions across tasks in both spatial and temporal dimensions.