Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.
@article{arxiv.2103.05401,
title = {Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping},
author = {Marc Tuscher and Julian Hörz and Danny Driess and Marc Toussaint},
journal= {arXiv preprint arXiv:2103.05401},
year = {2021}
}