Robotic dexterous grasping is the first step to enable human-like dexterous object
manipulation and thus a crucial robotic technology. However, dexterous grasping is much …

Dexterous grasping aims to produce diverse grasping postures with a high grasping
success rate. Regression-based methods that directly predict grasping parameters given the …

Generating dexterous grasping has been a long-standing and challenging robotic task.
Despite recent progress, existing methods primarily suffer from two issues. First, most prior …

This paper presents a real-time, object-independent grasp synthesis method which can be
used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural …

Learning robotic grasps from visual observations is a promising yet challenging task. Recent
research shows its great potential by preparing and learning from large-scale synthetic …

Multi-finger grasping relies on high quality training data, which is hard to obtain: human data
is hard to transfer and synthetic data relies on simplifying assumptions that reduce grasp …

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-of-
Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the …

While there exists many methods for manipulating rigid objects with parallel-jaw grippers,
grasping with multi-finger robotic hands remains a quite unexplored research topic …

Grasping with anthropomorphic robotic hands involves much more hand-object interactions
compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the …

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000
generated objects aimed at training and evaluating robotic visual grasp detection …