4 accepted papers
Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused on the overall prevalent action being executed, but we argue that bimanual human motion cannot always be described suff
The paper presents and evaluates a distance-aware dynamic roadmap (DA-DRM) algorithm as an extension of the dynamic roadmap (DRM) approach. In contrast to previous work, the algorithm is capable of planning collision-free trajectories while considering the distance to obstacles, even in unknown envi
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 depth images of the scene as its sole input for synthesis of a single-grasp solution during execution, adequately portraying the robo…
In this letter, we present a grasp planner that integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identif