publications

2026

1. 

   Dex-X: 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, Mingzhi Pei, Chenyu Zhang, Wanli Xing, Kaifeng Zhang, and Mengdi Xu^†

   In 2026 Conference on Robot Learning (CoRL), 2026

   RSS 2026 Workshop on Dexterous Manipulation (Spotlight)

   Abs arXiv 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.

2. 

   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.

3. 

   What Do VLAs Actually Learn through In-Context Failure Conditioning?

   Jiajun Liu, Jieming Li, Zi Zhuang, Hang Yu, Qingli Chen, Liu Cao, Yingxi Lu, Ruoqu Chen, Yuhang Cao, Chenyu Zhang, Yankai Lin, and Mengdi Xu^†

   In CVPR 2026 Workshop on 3D-LLM/VLA, 2026

   Abs Code Website

   Using RoboRetry as a controlled probe across 12 RLBench tasks, we study whether vision-language-action policies actually learn from prior failures via in-context conditioning, disentangling a slot-presence effect from content-dependent gains, and release FailureSlot, a dataset of 1,334 annotated failure trajectories.

4. 

   R2S-Eval: Robot Evaluation with Real-to-Sim Calibration via Vision-Language Models

   Yidi Wang, Feixiang Ruan, Ruoqu Chen, Jie Yin, Yang Yu, Mengdi Xu, and Kaifeng Zhang^†

   Preprint, 2026

   Abs arXiv Website

   An evaluation pipeline that pairs real-to-sim calibration with a vision-language model evaluator: calibrated simulator rollouts replace repeated hardware trials, and pairwise VLM preferences are aggregated into policy rankings, enabling automated, statistically stable, and quality-aware evaluation of robot behavior.

5. 

   Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching

   Chenyu Zhang, Yuhang Cao, Yingxi Lu, Daru Du, Jing Shao, Jiajun Liu, Ruoqu Chen, Liu Cao, Yicheng Liu, Hang Zhao, and Mengdi Xu^†

   Preprint, 2026

   Abs Website

   A rethinking of causal action tokenization that introduces conditional annealing within a flow-matching framework to improve action representation for robot learning.

6. 

   RoboCoach: World Models as Active Coaches for Compositional Robot Skills

   Jiajun* Liu, Yifan* Chen, Yichao* Liu, Jiayi Zhang, Ruoqu Chen, Shaoxuan Xie, Guocai Yao, Mengdi Xu^†, Sen Cui^†, and Changshui Zhang^†

   Preprint, 2026

   Abs Website

   RoboCoach treats world models as active coaches that guide robots in acquiring compositional skills.

2025

1. 

   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.

2. 

   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.