Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

Having a representation of the capabilities of a robot is helpful when online queries, such as
solving the inverse kinematics (IK) problem for grasping tasks, must be processed efficiently …

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 …

Large-scale human motion databases are key for research questions ranging from human
motion analysis and synthesis, biomechanics of human motion, data-driven learning of …

One of the recurring challenges in humanoid robotics is the development of learning
mechanisms to predict the effects of certain actions on objects. It is paramount to predict the …

A whole-body support pose taxonomy for multi-contact humanoid robot motions | Science
Robotics news careers commentary Journals Science Science brought to you byGoogle Indexer …

With ArmarX we introduce a robot programming environment that has been developed in
order to ease the realization of higher level capabilities needed by complex robotic systems …

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 …

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 …

This paper investigates theoretical properties of a well-known L 1 grasp quality measure Q
whose approximation Q− l is commonly used for the evaluation of grasps and where the …