  Grasping and manipulating objects is an important human skill. Since most
objects are designed to be manipulated by human hands, anthropomorphic hands
can enable richer human-robot interaction. Desirable grasps are not only
stable, but also functional: they enable post-grasp actions with the object.
However, functional grasp synthesis for high degree-of-freedom anthropomorphic
hands from object shape alone is challenging because of the large optimization
space. We present ContactGrasp, a framework for functional grasp synthesis from
object shape and contact on the object surface. Contact can be manually
specified or obtained through demonstrations. Our contact representation is
object-centric and allows functional grasp synthesis even for hand models
different than the one used for demonstration. Using a dataset of contact
demonstrations from humans grasping diverse household objects, we synthesize
functional grasps for three hand models and two functional intents. The project
webpage is https://contactdb.cc.gatech.edu/contactgrasp.html.
