75 citations · 92 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
Jun Lv, Qiaojun Yu, Lin Shao +3
Building general-purpose robots to perform a diverse range of tasks in a large variety of environments in the physical world at the human level is extremely challenging. It require…
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping
Hanwen Cao, Hao-Shu Fang, Wenhai Liu +1
Suction is an important solution for the longstanding robotic grasping problem. Compared with other kinds of grasping, suction grasping is easier to represent and often more reliab…
Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docx
Mingshuo Han, Wenhai Liu., Zhenyu Pan +4
In this paper we study grasp problem in dense cluster, a challenging task in warehouse logistics scenario. By introducing a two-step robust suction affordance detection method, we…
Bayesian Grasp: Robotic visual stable grasp based on prior tactile knowledge
Teng Xue, Wenhai Liu, Mingshuo Han +4
Robotic grasp detection is a fundamental capability for intelligent manipulation in unstructured environments. Previous work mainly employed visual and tactile fusion to achieve st…
Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter
Quanquan Shao, Jie Hu, Weiming Wang +4
This paper focuses on robotic picking tasks in cluttered scenario. Because of the diversity of poses, types of stack and complicated background in bin picking situation, it is much…