6 papers
Learning Object-Centric Motion Priors from Human for Robotic Dexterous Manipulation
Zhengdong Hong, Guofeng Zhang
Manipulating diverse objects with multi-fingered dexterous hands is challenging due to the high dimensionality and complex dynamics. Human-Object Interaction (HOI) datasets provide…
SyncHeC: Markerless Spatio-Temporal Hand-Eye Calibration via Differentiable Rendering and Continuous-Time Joint Optimization
Zhengdong Hong
Markerless hand-eye calibration under continuous motion requires jointly resolving camera geometry and the time offset between images and joint states. We present SyncHeC (Synchron…
LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation
Jingjing Wang, Zhengdong Hong, Chong Bao +3
Human-like generalization in open-world remains a fundamental challenge for robotic manipulation. Existing learning-based methods, including reinforcement learning, imitation learn…
R3D: Revisiting 3D Policy Learning
Zhengdong Hong, Shenrui Wu, Haozhe Cui +8
3D policy learning promises superior generalization and cross-embodiment transfer, but progress has been hindered by training instabilities and severe overfitting, precluding the a…
Learning Adaptive Dexterous Grasping from Single Demonstrations
Liangzhi Shi, Yulin Liu, Lingqi Zeng +3
How can robots learn dexterous grasping skills efficiently and apply them adaptively based on user instructions? This work tackles two key challenges: efficient skill acquisition f…
EasyHeC++: Fully Automatic Hand-Eye Calibration with Pretrained Image Models
Zhengdong Hong, Kangfu Zheng, Linghao Chen
Hand-eye calibration plays a fundamental role in robotics by directly influencing the efficiency of critical operations such as manipulation and grasping. In this work, we present…