4 citations · 12 across the 5 of their papers we have counts for
5 papers
Target Pose Guided Whole-body Grasping Motion Generation for Digital Humans
Quanquan Shao, Yi Fang
Grasping manipulation is a fundamental mode for human interaction with daily life objects. The synthesis of grasping motion is also greatly demanded in many applications such as an…
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…
Combining RGB and Points to Predict Grasping Region for Robotic Bin-Picking
Quanquan Shao, Jie Hu
This paper focuses on a robotic picking tasks in cluttered scenario. Because of the diversity of objects and clutter by placing, it is much difficult to recognize and estimate thei…