Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping
This addresses robotic manipulation in unknown environments, enabling practical applications in real-world settings, but it appears incremental as it builds on existing tracking and grasping techniques.
The paper tackles the problem of reactive grasping by developing a method for 6-DoF tracking of unknown objects, combining Siamese Networks with Iterative Closest Point for pointcloud registration, resulting in a system that can grasp a wide variety of unseen objects robustly against perturbations and noise.
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.