5 papers · 1 filter
How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation
Jiaju Yin, Zhenhui Zhang, Lixin Xu +5
Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the prohibitive cost of hardware fa…
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
Ke Zhang, Lixin Xu, Chengyi Song +4
Dexterous manipulation remains one of the most challenging problems in robotics, requiring coherent control of high-DoF hands and arms under complex, contact-rich dynamics. A major…
PDF-HR: Pose Distance Fields for Humanoid Robots
Yi Gu, Yukang Gao, Yangchen Zhou +7
Pose and motion priors play a crucial role in humanoid robotics. Although such priors have been widely studied in human motion recovery (HMR) domain with a range of models, their a…
Taming VR Teleoperation and Learning from Demonstration for Multi-Task Bimanual Table Service Manipulation
Weize Li, Zhengxiao Han, Lixin Xu +4
This technical report presents the champion solution of the Table Service Track in the ICRA 2025 What Bimanuals Can Do (WBCD) competition. We tackled a series of demanding tasks un…
DexFlow: A Unified Approach for Dexterous Hand Pose Retargeting and Interaction
Xiaoyi Lin, Kunpeng Yao, Lixin Xu +4
Despite advances in hand-object interaction modeling, generating realistic dexterous manipulation data for robotic hands remains a challenge. Retargeting methods often suffer from…