In this work, we present a part-based grasp planning approach that is capable of generating
grasps that are applicable to multiple familiar objects. We show how object models can be …

Robotic grasping in highly cluttered environments remains a challenging task due to the lack
of collision free grasp affordances. In such conditions, non-prehensile actions could help to …

With the increasing performance of machine learning techniques in the last few years, the
computer vision and robotics communities have created a large number of datasets for …

The use of benchmarks is a widespread and scientifically meaningful practice to validate
performance of different approaches to the same task. In the context of robot grasping the …

We study the problem of learning online packing skills for irregular 3D shapes, which is
arguably the most challenging setting of bin packing problems. The goal is to consecutively …

Grasping unknown objects with multi-fingered hands at high success rates and in real-time
is an unsolved problem. Existing methods are limited in the speed of grasp synthesis or the …

We report on an extensive study of the benefits and limitations of current deep learning
approaches to object recognition in robot vision scenarios, introducing a novel dataset used …

We present a dataset with models of 14 articulated objects commonly found in human
environments and with RGB-D video sequences and wrenches recorded of human …

Other keywords: deep learning; structural similarity; UAV systems; super-resolution
unmanned aerial vehicle imaging; low-resolution images; LR image; quantit ative analysis; …

We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates,
and refines grasps on partial object point clouds. DexDiffuser includes the conditional …