General robot grasping in clutter requires the ability to synthesize grasps that work for
previously unseen objects and that are also robust to physical interactions, such as …

Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete
and noisy perception. In this work, we draw insight that 3D reconstruction and grasp learning …

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires
both reasoning about unseen object parts and potential collisions with the manipulator. Most …

Fast and robust object grasping in clutter is a crucial component of robotics. Most current
works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the …

Grasping unseen objects in unconstrained, cluttered environments is an essential skill for
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …

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 …

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 across …

In this paper, we focus on the problem of feature learning in the presence of scale imbalance
for 6-DoF grasp detection and propose a novel approach to especially address the difficulty …

Grasping is among the most fundamental and long-lasting problems in robotics study. This
paper studies the problem of 6-DoF (degree of freedom) grasping by a parallel gripper in a …

We present a novel approach to perform object-independent grasp synthesis from depth
images via deep neural networks. Our generative grasping convolutional neural network …