Zeyuan Chen 陈泽源

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
arXiv / website

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.

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
arXiv / website

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.

Jiyao Zhang*, Junhan Wang*, Tianyu Wang*, Zeyuan Chen, Anthony Bolton, Yitong Peng, Hao Dong
[CoRL 2026] Conference on Robot Learning 2026 (Oral)
(one of 21 oral presentations among 687 accepted papers, selected from 2,094 submissions)
arXiv / website

We present a unified data and generative modeling framework for open-vocabulary task-oriented dexterous grasping. The method grounds free-form functional intent in visual and geometric observations to generate executable, task-consistent dexterous grasps.

Peiqi Liu, Jingwen Li, Zeyuan Chen, Yue Chen, Shuqi Zhao, Yuanpei Chen, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan, Ruihai Wu
[RSS Workshop 2026] 4th RSS Workshop on Dexterous Manipulation: Scalable Learning for Human-Level Skills

We present BiDexAffordance, an affordance-driven framework that predicts collaborative object-surface contact maps for efficient bimanual dexterous grasp synthesis. The learned priors guide lightweight physics-based optimization, improving simulated and real-world grasp success while generalizing to unseen objects.

Zeyuan Chen*, Qiyang Yan*, Yuanpei Chen*, Tianhao Wu, Jiyao Zhang, Zihan Ding, Jinzhou Li, Yaodong Yang, Hao Dong
[CoRL 2025] Conference on Robot Learning 2025 (Oral)
arXiv / website

We propose the first close-loop sim-to-real system for general dexterous grasping in cluttered scenes.

Jiyao Zhang*, Zhiyuan Ma*, Tianhao Wu**, Zeyuan Chen**, Hao Dong
[NeurIPS 2025] Neural Information Processing Systems 2025
website / code (coming soon)

We propose a contact- and collision-aware intermediate representation for general dexterous grasping in clutterred scenes.

Jinzhou Li*, Tianhao Wu*, Jiyao Zhang**, Zeyuan Chen**, Haotian Jin, Mingdong Wu, Yujun Shen, Yaodong Yang, Hao Dong
[IROS 2025] IEEE/RSJ International Conference on Intelligent Robots and Systems 2025
arXiv/ website

We propose a force diffusion & force-guided attention fusion module that adaptively adjusts the weights of visual and tactile features.