Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

Video attachement for paper:
Dmytro Pavlichenko, Diego Rodriguez, Christian Lenz, Max Schwarz, and Sven Behnke:
Autonomous Bimanual Functional Regrasping of Novel Object Class Instances
IEEE-RAS International Conference on Humanoid Robots (Humanoids), Toronto, Canada, October 2019.


Abstract:
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.

Grasping,Robot,Manipulation,Tool use,