  The progressive prevalence of robots in human-suited environments has given
rise to a myriad of object manipulation techniques, in which dexterity plays a
paramount role. It is well-established that humans exhibit extraordinary
dexterity when handling objects. Such dexterity seems to derive from a robust
understanding of object properties (such as weight, size, and shape), as well
as a remarkable capacity to interact with them. Hand postures commonly
demonstrate the influence of specific regions on objects that need to be
grasped, especially when objects are partially visible. In this work, we
leverage human-like object understanding by reconstructing and completing their
full geometry from partial observations, and manipulating them using a 7-DoF
anthropomorphic robot hand. Our approach has significantly improved the
grasping success rates of baselines with only partial reconstruction by nearly
30% and achieved over 150 successful grasps with three different object
categories. This demonstrates our approach's consistent ability to predict and
execute grasping postures based on the completed object shapes from various
directions and positions in real-world scenarios. Our work opens up new
possibilities for enhancing robotic applications that require precise grasping
and manipulation skills of real-world reconstructed objects.
