ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact

IROS 2019

Samarth Brahmbhatt1, Ankur Handa2, James Hays1, Dieter Fox2

Georgia Tech Robotics1, NVIDIA Robotics2


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-DOF anthropomorphic hands from object shape alone is challenging. We present ContactGrasp, a framework that allows 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.

We develop a grasp sampler based on GraspIt!, and a non-linear optimzer based on DART for optimizing grasps to agree with contact demonstrations from ContactDB. Here's a look at the process:
This video shows the grasps generated by our algorithm for GraspIt!'s HumanHand20DOF, the Allegro Hand, and the Barrett Hand, for two different post-grasp functional intents - 'use' and 'handoff':