ICGNet: A Unified Approach for Instance-Centric Grasping
René Zurbrügg Yifan Liu Francis Engelmann Suryansh Kumar Marco Hutter Vaishakh Patil Fisher Yu
Accurate grasping is the key to several robotic tasks including assembly and
household robotics. Executing a successful grasp in a cluttered environment
requires multiple levels of scene understanding: First, the robot needs to
analyze the geometric properties of individual objects to find feasible grasps.
These grasps need to be compliant with the local object geometry. Second, for
each proposed grasp, the robot needs to reason about the interactions with
other objects in the scene. Finally, the robot must compute a collision-free
grasp trajectory while taking into account the geometry of the target object.
Most grasp detection algorithms directly predict grasp poses in a monolithic
fashion, which does not capture the composability of the environment. In this
paper, we introduce an end-to-end architecture for object-centric grasping. The
method uses pointcloud data from a single arbitrary viewing direction as an
input and generates an instance-centric representation for each partially
observed object in the scene. This representation is further used for object
reconstruction and grasp detection in cluttered table-top scenes. We show the
effectiveness of the proposed method by extensively evaluating it against
state-of-the-art methods on synthetic datasets, indicating superior performance
for grasping and reconstruction. Additionally, we demonstrate real-world
applicability by decluttering scenes with varying numbers of objects.