  In human-made scenarios, robots need to be able to fully operate objects in
their surroundings, i.e., objects are required to be functionally grasped
rather than only picked. This imposes very strict constraints on the object
pose such that a direct grasp can be performed. Inspired by the anthropomorphic
nature of humanoid robots, we propose an approach that first grasps an object
with one hand, obtaining full control over its pose, and performs the
functional grasp with the second hand subsequently. Thus, we develop a fully
autonomous pipeline for dual-arm functional regrasping of novel familiar
objects, i.e., objects never seen before that belong to a known object
category, e.g., spray bottles. This process involves semantic segmentation,
object pose estimation, non-rigid mesh registration, grasp sampling, handover
pose generation and in-hand pose refinement. The latter is used to compensate
for the unpredictable object movement during the first grasp. The approach is
applied to a human-like upper body. To the best knowledge of the authors, this
is the first system that exhibits autonomous bimanual functional regrasping
capabilities. We demonstrate that our system yields reliable success rates and
can be applied on-line to real-world tasks using only one off-the-shelf RGB-D
sensor.
