CIMI: Contact Imitation Interface — Transferring Dexterous Skills by Showing How Contact Unfolds

Zhixuan Xu^*, Yichen Li^*, Bingchen Wu, Yiwen Hou, Zixuan Liu, Tianyu Qiu, Lin Shao

Transfer dexterous skills from robot-free human demonstrations across robot hands using shared contact geometry and fingertip RGB views.

FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing

Zhixuan Xu^*, Yichen Li^*, Xuanye Wu, Tianyu Qiu, Lin Shao

Conference on Robot Learning (CoRL) 2026

Spotlight Presentation

Combine fingertip binocular vision with contact-wrench cues from compliant-ring deformation for dexterous manipulation.

T(R,O) Grasp: Efficient Graph Diffusion of Robot-Object Spatial Transformation for Cross-Embodiment Dexterous Grasping

Xin Fei^*, Zhixuan Xu^*, Huaicong Fang, Tianrui Zhang, Lin Shao

International Conference on Robotics and Automation (ICRA) 2026

Use hand-object graph diffusion for cross-embodiment grasp synthesis, achieving 94.83% success with 0.21 s inference.

D(R,O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping

Zhenyu Wei*, Zhixuan Xu*, Jingxiang Guo, Yiwen Hou, Chongkai Gao, Zhehao Cai, Jiayu Luo, Lin Shao

International Conference on Robotics and Automation (ICRA) 2025

Best Paper Award on Robot Manipulation and Locomotion @ ICRA 2025

Best Paper Award Finalist @ ICRA 2025

Best Robotics Paper Award @ CoRL 2024 Workshop MAPoDeL

Represent robot-object interactions to generalize dexterous grasping across diverse robotic hands and objects.

ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots

Zhixuan Xu*, Chongkai Gao*, Zixuan Liu*, Gang Yang*, Chenrui Tie, Haozhuo Zheng, Haoyu Zhou, Weikun Peng, Debang Wang, Tianrun Hu, Tianyi Chen, Zhouliang Yu, Lin Shao

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024

Oral Presentation

Introduce a framework taking contact synthesis as a unified task representation that can generalizes over objects, robots, and manipulation tasks.

DexMimic: A Generalist Dexterous Hand–Object Tracking Policy via Value-Guided Reset Allocation

Zhexi Luo, Zhixuan Xu, Rui Yang, Zeyu Xiao, Yiwen Hou, Lin Shao

In Submission

One generalist policy for diverse dexterous skills from human demonstrations, with efficient learning and growing generalization as training scales.

T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale

Xin Fei, Zhixuan Xu, Huaicong Fang, Fangjun Chen, Zhaoyu Mu, Rui Yang, Zhexi Luo, Lin Shao

Conference on Robot Learning (CoRL) 2026

Scale diffusion-based dexterous grasping in clutter across eight robot hands and approximately 50K objects.

Web2Grasp: Learning Functional Grasps from Web Images of Hand-Object Interactions

Hongyi Chen*, Yunchao Yao*, Yufei Ye, Zhixuan Xu, Homanga Bharadhwaj, Jiashun Wang, Shubham Tulsiani, Zackory Erickson, Jeffrey Ichnowski

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026

Propose extracting human grasp from web images, from which we retarget, align, and enhance to create a robot grasp dataset.

QuaMa: Extending Skill Learning with Delta Dynamics Modeling for Precise and Generalizable Quantitative Manipulation

Yiwen Hou^*, Yaoyu Cheng^*, Jiayu Luo, Zhixuan Xu, Chongkai Gao, Jingxiang Guo, Lin Shao

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026

Combine skill learning, delta dynamics, and online action refinement for precise pouring across unseen containers, materials, and targets.

Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation

Zixuan Liu*, Ruoyi Qiao*, Chenrui Tie, Xuanwei Liu, Yunfan Lou, Chongkai Gao, Zhixuan Xu, Lin Shao

In Submission

Guide RL exploration with contact coverage to discover diverse contact patterns and improve dexterous manipulation learning.

DexSinGrasp: Learning a Unified Policy for Dexterous Object Singulation and Grasping in Cluttered Environments

Lixin Xu*, Zixuan Liu*, Zhewei Gui, Jingxiang Guo, Zeyu Jiang, Zhixuan Xu, Chongkai Gao, Lin Shao

IEEE Robotics and Automation Letters (RA-L) 2025

Introduce a unified policy for dexterous object singulation and grasping via clutter arrangement curriculum learning.

MetaFold: Language-Guided Multi-Category Garment Folding Framework via Trajectory Generation and Foundation Model

Haonan Chen*, Junxiao Li*, Ruihai Wu, Yiwei Liu, Yiwen Hou, Zhixuan Xu, Jingxiang Guo, Chongkai Gao, Zhenyu Wei, Shensi Xu, Jiaqi Huang, Lin Shao

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

Oral Presentation

Disentangle folding tasks into language-guided point cloud trajectory generation and low-level action prediction.

TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness

Jingxiang Guo*, Jiayu Luo*, Zhenyu Wei*, Yiwen Hou, Zhixuan Xu, Xiaoyi Lin, Chongkai Gao, Lin Shao

Best Paper Award @ ICRA 2025 Workshop on Human-Centric Teleoperation

Spotlight Presentation @ ICRA 2025 Workshop on Human-Centric Teleoperation

Use data gloves, IMU sensors, and previews of future robot motion for low-cost, more effective teleoperation data collection.

FLIP: Flow-Centric Generative Planning for General-Purpose Manipulation Tasks

Chongkai Gao, Haozhuo Zhang, Zhixuan Xu, Zhehao Cai, Lin Shao

International Conference on Learning Representations (ICLR) 2025

Oral Presentation @ CoRL 2024 Workshop LEAP

Propose flow-centric generative planning (FLIP) as an interactive world model for general-purpose model-based planning for manipulation tasks.

Diff-LfD: Contact-aware Model-based Learning from Visual Demonstration for Robotic Manipulation via Differentiable Physics-based Simulation and Rendering

Xinghao Zhu, Jinghan Ke, Zhixuan Xu, Zhixin Sun, Bizhe Bai, Jun Lv, Qingtao Liu, Yuwei Zeng, Qi Ye, Cewu Lu, Masayoshi Tomizuka, Lin Shao

Conference on Robot Learning (CoRL) 2023

Oral Presentation

Learn dexterous manipulation from human videos using differentiable rendering for pose and shape estimation and simulation for contact generation.