4 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.RO2019★ 4 cited
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…
cs.RO2019★ 3 cited
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…
cs.RO2019★ 3 cited
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…