  We propose a novel pipeline for unknown object grasping in shared robotic
autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are
typically learning-based approaches optimised for a specific end-effector, that
generate grasp poses directly from sensor input. In the domain of assistive
robotics, we seek instead to utilise the user's cognitive abilities for
enhanced satisfaction, grasping performance, and alignment with their high
level task-specific goals. Given a pair of stereo images, we perform unknown
object instance segmentation and generate a 3D reconstruction of the object of
interest. In shared control, the user then guides the robot end-effector across
a virtual hemisphere centered around the object to their desired approach
direction. A physics-based grasp planner finds the most stable local grasp on
the reconstruction, and finally the user is guided by shared control to this
grasp. In experiments on the DLR EDAN platform, we report a grasp success rate
of 87% for 10 unknown objects, and demonstrate the method's capability to grasp
objects in structured clutter and from shelves.
