6 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
Hao-Shu Fang, Hengxu Yan, Zhenyu Tang +3
We introduce an efficient approach for learning dexterous grasping with minimal data, advancing robotic manipulation capabilities across different robotic hands. Unlike traditional…
A Composable Framework for Policy Design, Learning, and Transfer Toward Safe and Efficient Industrial Insertion
Rui Chen, Chenxi Wang, Tianhao Wei +1
Delicate industrial insertion tasks (e.g., PC board assembly) remain challenging for industrial robots. The challenges include low error tolerance, delicacy of the components, and…
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
Minghao Gou, Hao-Shu Fang, Zhanda Zhu +3
General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most…
Transferable Active Grasping and Real Embodied Dataset
Xiangyu Chen, Zelin Ye, Jiankai Sun +4
Grasping in cluttered scenes is challenging for robot vision systems, as detection accuracy can be hindered by partial occlusion of objects. We adopt a reinforcement learning (RL)…