4 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge
Hengxu Yan, Haoshu Fang, Cewu Lu
Dexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the su…
A Surprisingly Efficient Representation for Multi-Finger Grasping
Hengxu Yan, Hao-Shu Fang, Cewu Lu
The problem of grasping objects using a multi-finger hand has received significant attention in recent years. However, it remains challenging to handle a large number of unfamiliar…
ManiPose: A Comprehensive Benchmark for Pose-aware Object Manipulation in Robotics
Qiaojun Yu, Ce Hao, Junbo Wang +6
Robotic manipulation in everyday scenarios, especially in unstructured environments, requires skills in pose-aware object manipulation (POM), which adapts robots' grasping and hand…
AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains
Hao-Shu Fang, Chenxi Wang, Hongjie Fang +6
As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous acro…