5 papers
Cross-Embodiment Dexterous Grasping with Reinforcement Learning
Haoqi Yuan, Bohan Zhou, Yuhui Fu +1
Dexterous hands exhibit significant potential for complex real-world grasping tasks. While recent studies have primarily focused on learning policies for specific robotic hands, th…
Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations
Bohan Zhou, Haoqi Yuan, Yuhui Fu +1
Bimanual dexterous manipulation is a critical yet underexplored area in robotics. Its high-dimensional action space and inherent task complexity present significant challenges for…
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping
Ziye Huang, Haoqi Yuan, Yuhui Fu +1
Universal dexterous grasping across diverse objects presents a fundamental yet formidable challenge in robot learning. Existing approaches using reinforcement learning (RL) to deve…
DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos
Haoqi Yuan, Ruihai Wu, Andrew Zhao +3
Learning an accurate model of the environment is essential for model-based control tasks. Existing methods in robotic visuomotor control usually learn from data with heavily labell…
DLGAN: Disentangling Label-Specific Fine-Grained Features for Image Manipulation
Guanqi Zhan, Yihao Zhao, Bingchan Zhao +3
Recent studies have shown how disentangling images into content and feature spaces can provide controllable image translation/ manipulation. In this paper, we propose a framework t…