Large, richly annotated datasets have accelerated progress in fields such as computer
vision and natural language processing, but replicating these successes in robotics has …

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 …

In this article, a six-step approach is proposed to simulate the grasp and evaluate the grasp
quality for a versatile vacuum gripper by tracking the deformation and force-torque wrench of …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

We present a data-driven, bottom-up, deep learning approach to robotic grasping of
unknown objects using Deep Convolutional Neural Networks (DCNNs). The approach uses …

Interpreting objects with basic geometric primitives has long been studied in computer
vision. Among geometric primitives, superquadrics are well known for their ability to …

Automated shape repair approaches currently lack access to datasets that describe real-
world damaged geometry. We present Fantastic Breaks (and Where to Find Them …

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of
hand poses in SE (3) from which an object can be successfully grasped. This important …

Semi-autonomous control strategies for prosthetic hands provide a promising way to simplify
and improve the grasping process for the user by adopting techniques usually applied in …

We present an end-to-end algorithm for training deep neural networks to grasp novel
objects. Our algorithm builds all the essential components of a grasping system using a …