  An important prerequisite for autonomous robots is their ability to reliably
grasp a wide variety of objects. Most state-of-the-art systems employ
specialized or simple end-effectors, such as two-jaw grippers, which severely
limit the range of objects to manipulate. Additionally, they conventionally
require a structured and fully predictable environment while the vast majority
of our world is complex, unstructured, and dynamic. This paper presents an
implementation to overcome both issues. Firstly, the integration of a
five-finger hand enhances the variety of possible grasps and manipulable
objects. This kinematically complex end-effector is controlled by a deep
learning based generative grasping network. The required virtual model of the
unknown target object is iteratively completed by processing visual sensor
data. Secondly, this visual feedback is employed to realize closed-loop servo
control which compensates for external disturbances. Our experiments on real
hardware confirm the system's capability to reliably grasp unknown dynamic
target objects without a priori knowledge of their trajectories. To the best of
our knowledge, this is the first method to achieve dynamic multi-fingered
grasping for unknown objects. A video of the experiments is available at
https://youtu.be/Ut28yM1gnvI.

Robotics